As artificial intelligence evolves, there is increasing excitement about executing AI workloads on embedded devices using small language models (SLM).  
 
Arm’s recent demo, inspired by Microsoft’s “Tiny Stories” paper and Andrej Karpathy’s TinyLlama2 project, where a small language model trained on 21 million stories generates text, showcases endpoint AI’s potential for IoT and edge computing. In the demo, a user inputs a sentence, and the system generates an extended children’s story based on it. 
 
Our demo featured Arm’s Ethos-U85 NPU (Neural Processing Unit) running a small language model on embedded hardware. While large language models (LLMs) are more widely known, there is growing interest in small language models due to their ability to deliver solid performance with significantly fewer resources and lower costs, making them easier and cheaper to train.  

Implementing A Transformer-based Small Language Model on Embedded Hardware

Our demo showcased the Arm Ethos-U85 as a small, low-power platform capable of running generative AI, highlighting that small language models can perform well within narrow domains. Although TinyLlama2 models are simpler than the larger models from companies like Meta, they are ideal for showcasing the U85’s AI capabilities. This makes them a great fit for endpoint AI workloads. 

Developing the demo involved significant modeling efforts, including the creation of a fully integer int8 (and int8x16) Tiny Llama2 model, which was converted to a fixed-shape TensorFlow Lite format suitable for the Ethos-U85’s constraints.  
 
Our quantization approach has shown that fully integer language models can successfully balance the tradeoff between maintaining strong accuracy and output quality. By quantizing activation, normalization functions, and matrix multiplications, we eliminated the need for floating-point computations, which are more costly in terms of silicon area and energy—key concerns for constrained embedded devices.  
 
The Ethos-U85 ran a language model on an FPGA platform at only 32 MHz, achieving text generation speeds of 7.5 to 8 tokens per second—matching human reading speed—while using just a quarter of its compute capacity. In a real system-on-chip (SoC), performance could be up to ten times faster, significantly enhancing speed and energy efficiency for AI processing at the edge. 

The children’s story-generation feature used an open-source version of Llama2, running the demo on TFLite Micro with an Ethos-NPU back-end. Most of the inference logic was written in C++ at the application level. Adjusting the context window enhanced narrative coherence, ensuring smooth, AI-driven storytelling.  
 
The team’s adaptation of the Llama2 model to run efficiently on the Ethos-U85 NPU required careful consideration of performance and accuracy due to the hardware limitations. Using mixed int8 and int16 quantization demonstrates the potential of fully integer models, encouraging the AI community to optimize generative models for edge devices and expand neural network accessibility on power-efficient platforms like the Ethos-U85. 

Showcasing the Power of the Arm Ethos-U85

Scalable from 128 to 2048 MAC units (multiply-accumulate units), the Ethos-U85 achieves a 20% power efficiency improvement over its predecessor, the Ethos-U65. A standout feature of the Ethos-U85 is its native support for transformer networks, which earlier versions could not support.  
 
The Ethos-U85 enables seamless migration for partners using previous Ethos-U NPUs, allowing them to capitalize on existing investments in Arm-based machine learning tools. Developers are increasingly adopting the Ethos-U85 for its power efficiency and high performance.

The Ethos-U85 can reach 4 TOPS (trillions of operations per second) with a 2048 MAC configuration in silicon. In the demo, however, a smaller configuration of 512 MACs on an FPGA was used to run the Tiny Llama2 small language model with 15 million parameters at just 32 MHz.   
 
This capability highlights the potential for embedding AI directly into devices. The Ethos-U85 effectively handles such workloads even with limited memory (320 KB of SRAM for caching and 32 MB for storage), paving the way for small language models and other AI applications to thrive in deeply embedded systems. 

Bringing Generative AI to Embedded Devices

Developers need better tools to navigate the complexities of AI at the edge, and Arm is addressing this with the Ethos-U85 and its support for transformer-based models. As edge AI becomes more prominent in embedded applications, the Ethos-U85 is enabling new use cases, from language models to advanced vision tasks.  
 
The Ethos-U85 NPU delivers the performance and power efficiency required for innovative, cutting-edge solutions. Like the “Tiny Stories” paper, our demo represents a significant advancement in bringing generative AI to embedded devices, demonstrating the ease of deploying small language models on the Arm platform.  
 
Arm is opening new possibilities for Edge AI across a wide range of applications, positioning the Ethos-U85 to power the next generation of intelligent, low-power devices.  

Read how Arm is accelerating real-time processing for edge AI applications in IoT with ExecuTorch.

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When you’re driving hard to disrupt quantum computing paradigms, sometimes it’s smart to chill out. 

That’s Equal1’s philosophy. The Ireland-based company has notched another milestone on its journey deeper into the rapidly evolving field of quantum computing. Building on its success as winners of the “Silicon Startups Contest” in 2023, Equal1 has successfully tested the first chip incorporating an Arm Cortex processor at an astonishing temperature of 3.3 Kelvin (-269.85°C). That’s just a few degrees warmer than absolute zero, the theoretical lowest possible temperature where atomic motion nearly stops.

Equal1’s achievement is a crucial step in integrating classical computing components within the extremely power-constrained environment of a quantum cryo chamber. This brings the world closer to practical, scalable quantum computing systems. Cold temperatures reduce thermal noise that can cause errors in quantum computations and preserve quantum “coherence” – the ability of qubits to exist in multiple states simultaneously.

The Importance of Cryogenic Temperatures in Quantum Computing

What sets Equal1 apart in the quantum computing landscape is its pragmatic approach to quantum integration. Rather than creating entirely new infrastructure, Equal1’s vision was to build upon the foundation of the well-established semiconductor industry. This strategy became viable with the emergence of fully depleted silicon-on-insulator (FDSOI) processes, which the company’s founders recognized as having the potential to support quantum operations.

“Our thesis is that rather than tear up everything we’ve done and start anew, let’s try to build on top of what we’ve already built,” said Jason Lynch, CEO of Equal1. This philosophy has led to partnerships with industry leaders like Arm and NVIDIA, leveraging existing semiconductor expertise while pushing into quantum territory.

Cryo-Temperature Breakthrough

What makes this accomplishment particularly remarkable is the extensive engineering required to make it possible. 

“There is no such thing as a Spice Kit that works, that predicts what silicon is going to do at 3 Kelvin,” said Brendan Barry, Equal1’s CTO. “In fact, there’s no such thing as a methodology, no libraries you can get to make it happen.” 

Over five years, Equal1, which is part of the Arm Flexible Access program, developed its own internal Process Design Kit (PDK) and methodologies to predict and optimize logic behavior at cryogenic temperatures.

Equal1’s approach uses electrons or holes (the absence of electrons) as qubits, making their technology uniquely compatible with standard CMOS manufacturing processes. This choice wasn’t accidental; it’s fundamental to the company’s vision of creating practical, manufacturable quantum computers.

Working with commercial CMOS Fabs, Equal1 uses a standard process with proprietary design techniques developed over six years of research. These techniques enable operation at cryogenic temperatures while maintaining manufacturability. 

“We’re not changing anything in the process itself, but we are certainly pushing the limits of what the process can do,” Barry said.

Integrating the Arm Cortex-A55 Processor

Building on this success, Equal1 is now setting its sights even higher. The company plans to incorporate the more powerful Arm Cortex-A55 processor into its next-generation Quantum System-on-Chip (QSoC). This ambitious project aims to have silicon available by mid-2025, the company said.

The integration of Arm technology is crucial not just for processing power, but for power efficiency. At cryogenic temperatures, power management becomes critical as any heat generated can affect the quantum states. Arm’s advanced power-management features make it an ideal choice for this challenging environment.

Equal1’s technology targets three primary application areas:

• Chemistry and drug discovery, potentially reducing the current 15-year, $1.3 billion average cost of bringing new drugs to market.
• Optimization problems in finance, logistics, and other fields requiring complex variable management.
• Quantum AI applications, where quantum computing could dramatically improve efficiency.

Perhaps most revolutionary is Equal1’s approach to deployment. Unlike traditional quantum computers that require specialized facilities, Equal1 envisions rack-mounted quantum computers that can be installed in standard data centers at a fraction of the cost of current solutions. 

“They just rack in like any other standard high-performance compute,” said Patrick McNally, Equal1’s marketing lead.

The Road Ahead for Quantum Computing and Equal1

Equal1’s progress brings the world closer to the reality of compact, powerful quantum computers that can be deployed in standard high-performance computing environments. The company’s integration of Arm technology at cryogenic temperatures opens new possibilities for quantum-classical hybrid systems, potentially creating increased demand for Arm adoption across the quantum computing industry.

As quantum computing continues to evolve, Equal1’s practical approach to integration with existing semiconductor technology and infrastructure could prove to be a game-changer. With applications ranging from drug discovery to financial modeling and beyond, the future of quantum computing is looking increasingly accessible and practical.

And that’s pretty cool.

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Sir Robin Saxby, the founding CEO and former chairman of Arm, has been inducted into the City of London Engineering Hall of Fame. The ceremony, which took place on the High Walkway of Tower Bridge in London on October 31, 2024, announced the induction of seven iconic engineers who are from or connected to the City of London.

As Professor Gordon Masterton, Past Master Engineer, said: “The City of London Engineering Hall of Fame was launched in 2020 and now has 14 inductees whose lives tell the story of almost 500 years of world-beating engineering innovations that have created huge improvements in the quality of life and economy of the City of London, the United Kingdom and the world. Our mission is to celebrate these role models of exciting and inspirational engineering careers.”

Saxby joined Arm full-time as the first CEO in February 1991 where he led the transformation of the company from a 12-person startup to one of the most valuable tech companies in the UK with a market capitalization of over $10 billion.

As CEO, Saxby was the visionary behind Arm’s highly successful business model, which has been adopted by many other companies across the tech industry. Through this innovative business model, the Arm processor can be licensed to many different companies for an upfront license fee, with Arm receiving royalties based on the amount of silicon produced.

This paved the way for Arm to become the industry’s highest-performing and most power-efficient compute platform, with unmatched scale today touching 100 percent of the connected global population.

Under Saxby’s tenure at Arm, power-efficient technology became the foundation of the world’s first GSM mobile phones that achieved enormous commercial success during the 1990s, including the Arm-powered Nokia 6110. Today, more than 99 percent of the world’s smartphones are based on Arm technology. The success in the mobile market gave the company the platform to expand into other technology markets that require leading power-efficient technology from Arm, including IoT, automotive and datacenter.

Saxby stepped down as CEO in 2001 and chairman of Arm in 2006. In 2002, he was knighted in the 2002 New Year Honors List. Saxby is a visiting Professor at the University of Liverpool, a fellow of the Royal Academy of Engineering and an honorary fellow of the Royal Society.

Thanks to Saxby’s work, Arm has grown to be a global leader in technology, with nearly 8,000 employees worldwide today. Just as Saxby and the 12 founding members had originally envisioned, Arm remains committed to developing technology that will power the future of computing.

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As we move further into the era of advanced computing, Arm is continuing to lead the charge with groundbreaking tech innovations. October 2024 has been a month of significant strides in technology, particularly in AI, machine learning (ML), security, and system-on-chip (SoC) architecture.  

The Arm Editorial Team has highlighted the cutting-edge tech innovations that happened at Arm in October 2024 – all to shape the next generation of intelligent, secure, and high-performing compute systems. 

Enhancing AI, ML, and Security for Next-Gen SoCs with Armv9.6-A

Arm’s latest CPU architecture, Armv9.6-A, introduces key enhancements to meet evolving computing needs, focusing on AI, ML, security, and chiplet-based systems-on-chip (SoCs). Martin Weidmann, Director Product Management, discusses the latest features in the Arm A-Profile architecture for 2024. 

The 2024 updates enhance Scalable Matrix Extension (SME) with structured sparsity and quarter-tile operations for efficient matrix processing while improving memory management, resource partitioning, secure data handling, and multi-chip system support. 

Streamlining PyTorch Model Deployment on Edge Devices with ExecuTorch on Arm

Arm’s collaboration with Meta has led to the introduction of ExecuTorch, enhancing support for deploying PyTorch models on edge devices, particularly with the high-performing Arm Ethos-U85 NPU. Robert Elliott, Director of Applied ML, highlights how this collaboration enables developers to significantly reduce model deployment time and utilize advanced AI inference workloads with better scalability. 

With an integrated GitHub repository providing a fully supported development environment, ExecuTorch simplifies compiling and running models, allowing users to create intelligent IoT applications efficiently.  

Accelerating AI with Quantized Llama 3.2 Models on Arm CPUs

Arm and Meta have partnered to empower the AI developer ecosystem by enabling the deployment of quantized Llama 3.2 models on Arm CPUs with ExecuTorch and KleidiAI. Gian Marco Iodice, Principal Software Engineer, details how this integration allows quantized Llama 3.2 models to run up to 20% faster on Arm Cortex-A CPUs, while maintaining model quality and reducing memory usage. 

With the ExecuTorch beta release and support for lightweight quantized Llama 3.2 models, Arm is simplifying the development of AI applications for edge devices, resulting in notable performance gains in prefill and decode phases.  

Optimizing Shader Performance with Arm Performance Studio 2024.4

Arm’s latest Frame Advisor enhancement helps mobile developers identify inefficient shaders, boosting performance, memory usage, and power efficiency. Julie Gaskin, Staff Developer Evangelist, details the new features in Arm Performance Studio 2024.4, including support for new CPUs, improved Vulkan and OpenGL ES integration, and expanded RenderDoc debugging tools.   

This update provides detailed shader metrics – like cycle costs, register usage, and arithmetic precision – enabling developers to optimize performance and lower costs.  

Boosting Performance and Security for Arm Architectures with LLVM 19.1.0 

LLVM 19.1.0, released in September 2024, introduces nearly 1,000 contributions from Arm, including new architecture support for Armv9.2-A cores and performance improvements for data-center CPUs like Neoverse-V3. Volodymyr Turanskyy, Principal Software Engineer, highlights the features of LLVM 19.1.0, which deliver better performance and enhanced security.   
  
The update optimizes shader performance and Fortran intrinsics, adds support for Guarded Control Stack (GCS), security mitigations for Cortex-M Security Extensions (CMSE), enhancements for OpenMP reduction, function multi-versioning, and new command-line options for improved code generation. 

Introducing System Monitoring Control Framework (SMCF) for Neoverse CSS

Arm’s System Monitor Control Framework (SMCF) streamlines sensor and monitor management in complex SoCs with a standardized software interface. Marc Meunier, Director of Ecosystem Development, highlights how it supports seamless integration of third-party sensors, flexible data sampling, and efficient data collection through DMA, reducing processor overhead.   
  
The SMCF enables distributed power management and improves system telemetry, offering insights for profiling, debugging, and remote management while ensuring secure, standards-compliant data handling.   

Achieving Human-Readable Speeds with Llama 3 70B on AWS Graviton4 CPUs  

AWS’s Graviton4 processors, built with Arm Neoverse V2 CPU cores, are designed to boost cloud performance for high-demand AI workloads. Na Li, ML Solutions Architect, explains how deploying the Llama 3 70B model on Graviton4 leverages quantization techniques to achieve token generation rates of 5-10 tokens per second.   

This innovation enhances cloud infrastructure, enabling more powerful AI applications and improving performance for tasks requiring advanced reasoning.   

Superior Performance on Arm CPUs with Pardiso Sparse Linear Solver

Panua Technologies optimized the Pardiso sparse linear solver for Arm CPUs, delivering significant performance gains over Intel’s MKL. David Lecomber, Senior Director Infrastructure Tools, highlights how Pardiso on Arm Neoverse V1 processors outperform MKL, demonstrating superior efficiency and scalability for large-scale scientific and engineering computations.   

This breakthrough positions Pardiso as a top choice for industries like automotive manufacturing and semiconductor design, offering unmatched speed and performance.   

Built on Arm Partner Stories

Vince Hu, Corporate Vice President, MediaTek, talks about the Arm MediaTek partnership, which drives ongoing tech innovation and delivers transformative technologies to enhance everyday life. 

Eben Upton, CEO of Raspberry Pi, shares how the company has evolved from an educational tool to a key player in industrial and embedded applications, all powered by Arm technology. He highlights the development of new tools over the past decade and his personal journey with the BBC Microcomputer.  

Clay Nelson, Industry Solutions Strategy Lead at GitHub, discusses the partnership between GitHub and Arm, which combines GitHub Actions with Arm native hardware to revolutionize software development, leading to faster development times and reduced costs. 

Sy Choudhury from Meta Platforms Inc. explains how the collaboration with Arm is optimizing AI on the Arm Compute Platform, enhancing digital interactions through devices like AR smart glasses, and impacting everyday experiences with advanced AI applications.  

Highlights from PyTorch Conference 2024 

To accelerate the development of custom silicon solutions, Arm partners are tapping into the latest industry expertise and resources. Principal Software Engineer, Gian Marco Iodice discusses this in, “Democratizing AI: Powering the Future with Arm’s Global Compute Ecosystem,” from PyTorch Conference 2024. 

Iodice highlights KleidiAI-accelerated demos, key AI tech innovations from cloud to edge, and the latest Learning Paths for developers. 

The post What are the Latest Tech Innovations from Arm in October 2024? appeared first on Arm Newsroom.

One key message emerged from Open Compute Project (OCP) Global Summit 2024: AI scalability won’t progress unless we reduce design and development friction.

As the annual Silicon Valley event drew to a close, this crucial insight reverberated through the halls and informed discussions on the future of AI infrastructure and sustainable computing. 

While everyone understands the enormous opportunities, they also know the AI scalability challenges: 

• Unprecedented demands on computing infrastructure with AI model parameter counts doubling every 3.4 months.
• Global datacenter energy consumption is projected to triple by 2030.
• The traditional approach to chip design and development is struggling to keep pace with these demands, both in terms of performance and energy efficiency.
• As we push towards increasingly smaller process nodes, such as the current 3nm and the upcoming 2nm, the complexity and cost of manufacturing skyrocket. Design costs for a 2nm chip are estimated at a staggering $725 million. 

These challenges are compounded by the long development cycles of monolithic chips, which can take years from concept to production – a timeframe that’s increasingly out of sync with the rapid evolution of AI workloads. Additionally, the one-size-fits-all approach of traditional chip design is ill-suited to the diverse and specialized computing needs of modern AI applications, leading to inefficiencies in both performance and energy use.

Arm, as a key player in the semiconductor industry, is at the forefront of addressing this challenge, and at OCP, the company showcased innovative solutions and collaborative approaches to overcome the hurdles in AI scalability.

Eddie Ramirez (far left, image below), VP of Go to Market, Infrastructure at Arm, highlighted this challenge in his executive session, emphasizing that “the insatiable demand for AI performance is putting immense pressure on today’s datacenters. We need a paradigm shift in how we approach silicon design and development to meet these growing needs sustainably.”

Bottom image, from left: Eddie Ramirez (VP of Go to Market, Arm), Melissa Massa (Lenovo Global Sales Lead for cloud service providers), Thomas Gardens (VP of Solutions, Supermicro), Moderator Bill Carter (OCP).

Arm’s Vision: Reducing Friction Through Innovation

At OCP 2024, Arm presented a comprehensive strategy to address the AI scalability challenge, centered around two key pillars: chiplet technology and ecosystem collaboration.

Chiplets, the next frontier in silicon innovation, were a central theme in Arm’s OCP presence, representing a revolutionary approach to chip design that promises to reduce development friction significantly. By breaking down complex chip designs into smaller, modular components, chiplets offer several advantages:

• Lower development costs: Chiplets reduce manufacturing costs and improve yields compared to traditional monolithic designs.
• Faster time-to-market: The modular nature of chiplets allows for quicker iteration and product launches.
• Scalable performance: Companies can mix and match chiplets to create customized solutions for specific AI workloads.
• Improved power efficiency: Chiplet designs enable more granular power management, crucial for sustainable AI infrastructure.

In Arm’s Expo Hall session, “Accelerating AI Innovation with Arm Total Design: A Case Study,” Arm experts collaborated with Samsung to demonstrate how Arm Neoverse Compute Subsystems (CSS) rapidly brought an AI chiplet to market. This real-world example showcased the practical benefits of chiplet technology in reducing design and development friction.

Arm Total Design: Fostering Ecosystem Collaboration

Recognizing that no single company can solve the AI scalability challenge alone, Arm touted its Arm Total Design initiative, which has doubled in size in just one year. This collaborative approach brings together over 50 industry partners – foundries, third-party IP and EDA tools, design services, and OEMs – to create a standardized Chiplet System Architecture, ensuring interoperability and reusability of chiplet components across the ecosystem.

At OCP 2024, Arm highlighted new and expanding elements of the Arm Total Design:

• The introduction of new partners such as Egis, PUFsecurity, GUC, and Marvell, expanding the range of available chiplet solutions.
• A showcase of diverse chiplet applications, from AI accelerators to networking and edge computing solutions.
• The unveiling of a new AI CPU Chiplet Platform in collaboration with Samsung Foundry, ADTechnology, and Rebellions, promising approximately 3x power and performance efficiency compared to existing solutions.

All of these, taken in the aggregate, are meant to speed time to innovation, which is the name of the game.

Building a Sustainable AI Infrastructure

As we address the friction in AI development, sustainability remains a core focus. Arm’s approach to silicon design, particularly through chiplets and advanced process nodes, is crucial for building energy-efficient AI systems that can scale to meet future demands.

Arm’s initiatives for sustainable AI include demonstrating how Arm-based solutions can be optimized for specific AI workloads, improving overall system efficiency and collaborating on system firmware and chiplet standards to ensure interoperability and reduce development complexity, all contributing to AI scalability.

The Path Forward: Scaling AI Through Collaboration

The future of AI infrastructure lies in collaborative innovation. The challenges of scaling AI capabilities while maintaining energy efficiency are significant, but the advancements in chiplet technology and ecosystem-wide collaboration are paving the way for a more sustainable and scalable AI future.

Arm is committed to leading this transformation, providing the foundation for sustainable AI infrastructure through our Neoverse technology, Arm Total Design initiative, and extensive ecosystem partnerships.

So, what’s next?

• Expanded ecosystem collaboration: We’ll continue to grow the Arm Total Design network, bringing more partners into the fold to accelerate chiplet innovation and reduce development friction.
• Advanced AI optimizations: Expect to see more Arm-based solutions specifically tailored for AI training and inference workloads, designed to scale efficiently with growing demands.
• Sustainability-first approach: Energy efficiency will remain at the forefront of our design philosophy, helping our partners build more sustainable datacenter solutions that can support the exponential growth of AI.

OCP Global Summit 2024 served as a powerful reminder that the future of AI depends on our ability to innovate at the silicon level. By reducing design and development friction through chiplet technology and ecosystem collaboration, we’re opening new possibilities for scalable, sustainable AI infrastructure.

Eddie Ramirez put it best in his summit presentation: “The AI datacenter of tomorrow is being built today, and it’s being built on Arm.” With our technology at its core and our partners by our side, we’re not just scaling AI – we’re shaping a more efficient, sustainable future for computing.

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eXtended Reality (XR) is a term for technologies that enhance or replace our view of the world. This is often done by overlaying or immersing digital information and graphics into real-world and virtual environments, or even a combination of both, a process also known as spatial computing. 

XR encompasses augmented reality (AR), virtual reality (VR), and mixed reality (MR). While all three ‘realities’ share overlapping features and requirements, each technology has different purposes and underlying technologies. 

XR is set to play a fundamental role in the evolution of personal devices and immersive experiences, from being a companion device to smartphones to standalone Arm-powered wearable devices, such as a VR headset or pair of AR smartglasses where real, digital and virtual worlds converge into new realities for the user.  

While XR devices vary based on the type of AR, MR, and VR experiences and the complexity of the use cases that they are designed to enable, the actual technologies share some fundamental similarities. A core part of all XR wearable devices is the ability to use input methods, such as object, gesture, and gaze tracking, to navigate the world and display context-sensitive information. Depth perception and mapping are also enabled through the depth and location features.  

What are the advantages and challenges with XR?

XR technologies offer several advantages compared to other devices, including:  

• Enhanced interaction through more natural and intuitive user interfaces;  
• More realistic simulations for training and education;  
• Increased productivity with virtual workspaces and remote collaboration;  
• More immersive entertainment experiences; and  
• Alternative interaction methods for people with disabilities.  

However, there are still challenges that need to be overcome with XR devices. These include:  

• The initial perception around bulky and uncomfortable hardware.  
• Limited battery life for untethered devices.  
• Complex and resource-intensive content creation.  
• The need for low latency, and high performance.
• Ensuring data privacy and security for the end-user. 

What is augmented reality (AR)?

Augmented Reality enhances our view of the real world by overlaying what we see with computer-generated information. Today, this technology is prevalent in smartphone AR applications that require the user to hold their phone in front of them. By taking the image from the camera and processing it in real time, the app can display contextual information or deliver gaming and social experiences that appear to be rooted in the real world. 

Smartphone AR has improved significantly in the past decade, with some great examples being Snapchat where users can apply real-time face filters via AR, and IKEA place where users can visualize furniture in their homes through AR before making a purchase. However, the breadth of these applications remains limited. Increasingly, the focus is on delivering a more holistic AR experience through wearable smart glasses. These devices must combine an ultra-low-power processor with multiple sensors, including depth perception and tracking, all within a form factor that is light and comfortable enough to wear for long periods. 

AR smart glasses need always-on, intuitive, and secure navigation while users are on the move. This requires key advancements in features such as depth, 3D SLAM, semantics, location, orientation, position, pose, object recognition, audio services, and gesture and eye tracking. 

All these advancements and features will require supporting AI and machine learning (ML) capabilities on top of traditional computer vision (CV). In fact, new compact language models, which are designed to run efficiently on smaller devices, are becoming more influential across XR wearable technologies. These models enable real-time language processing and interaction, which means XR wearable devices can understand and respond to natural language in real time, allowing users to interact with XR applications using real-time voice commands.  

Since 2021, several smart glasses models have arrived on the market, including the Spectacle smartglasses from Snap, Lenovo ThinkReality A3, and in 2024, the Ray-Ban Meta Smart Glasses, Amazon Echo Frames, and, most recently, Meta’s Orion smartglasses. All of the devices are examples of how XR wearables are evolving to provide enhanced capabilities and features, like advanced AR displays and real-time AI video processing. 

What is virtual reality (VR)?

VR completely replaces a user’s view, immersing them within a computer-generated virtual environment. This type of XR technology has existed for a while, with gradual improvements over the years. It is used primarily for entertainment experiences, such as gaming, concerts, films, or sports but it’s also accelerating into the social domain. For VR, the immersive entertainment experiences will require capabilities like an HD rendering pipeline, volumetric capture, 6DoF motion tracking, and facial expression capture. 

VR is also used as a tool for training and in education and healthcare, such as rehabilitation. To make these experiences possible (and seamless) for the end-user, the focus of VR technology is often on high-quality video and rendering and ultra-low latency. 

Finally, VR devices started enhancing video conferencing experiences through platforms like Meta’s Horizon Workrooms that enable virtual meet-ups in different virtual worlds.  

Standalone VR devices, such as the latest Meta Quest 3, can deliver AAA gaming and online virtual worlds experiences. Powered by high-end Arm processors, these standalone VR devices can be taken anywhere.  

What is mixed Reality (MR)?

MR sits somewhere between AR and VR, as it merges the real and virtual worlds. There are three key scenarios for this type of XR technology. The first is through a smartphone or AR wearable device with virtual objects and characters superimposed into real-world environments, or potentially vice versa. 

The Pokémon Go mobile game, which took the world by storm back in 2016, overlays virtual Pokémon in real-world environments via a smartphone camera. This is often touted as a revolutionary AR game, but it’s actually a great example of MR – blending real-world environments with computer-generated objects. 

MR is revolutionizing the way we experience video games by enabling the integration of real-world players into virtual environments. This technology allows VR users to be superimposed into video games, creating a seamless blend of physical and digital worlds. As a result, real-world personalities can now interact within the game itself, enhancing the immersive experience for both players and viewers. 

This innovation is particularly impactful for streaming platforms like Twitch and YouTube. Streamers can now bring their unique personalities directly into the game, offering a more engaging and interactive experience for their audience. Viewers can watch their favorite streamers navigate virtual worlds as if they were part of the game, blurring the lines between reality and the digital realm. By incorporating MR, streamers can create more dynamic and visually captivating content, attracting larger audiences and fostering a deeper connection with their fans. This technology not only enhances the entertainment value but also opens up new possibilities for creative expression and storytelling in the gaming community.

XR is becoming more mainstream

With more XR devices entering the market, XR is becoming more affordable, with the technology transitioning from tech enthusiasts to mainstream consumers. 

XR is increasing the immersive experience, by adding more sensory inputs, integrating with more wearable technologies, and using generative AI to create faster and more realistic and interactive virtual environments for collaboration and meeting spaces, such as the Meta Horizon OS that will provide workspaces for regular work. This makes XR technologies more accessible and universally adopted in more markets beyond gaming, including: 

• Education e.g. Immersive simulation, exploring historical events virtually, virtual experiments, and augmenting museum tours 
• Healthcare e.g. More realistic medical training, removing the need for physical consultations, therapeutic applications, and AR-assisted surgeries 
• Retail: e.g. Virtual clothes fittings, product visualization, virtual shopping tours. 
• Industrial: e.g. Interactive training programs. 

XR is continuously evolving, increasingly at a faster pace thanks to the support of AI. This offers new ways to blur the frontier between virtual and physical worlds. 

Advancing XR Experiences 

Arm focuses on developing technology innovations that power the next generation of XR devices. Arm CPU and GPU technology delivers many benefits:   

• Efficient performance: Arm’s leadership in high-performance, low-power specialized processors is ideal for XR experiences. This includes the Cortex-X and Cortex-A CPUs as part of the new Arm Compute Subsystem (CSS) for Client that can be used in silicon solutions for wearables and mobile devices, providing the necessary compute capabilities for immersive XR experiences. These can also be combined with accelerator technologies like Ethos-U NPUs that can with Cortex-A-based systems to deliver accelerated AI performance. 
• Graphics capabilities: Arm’s Immortalis and Mali GPUs deliver exceptional graphics performance and efficiency for XR gaming experiences. 
• Real-Time 3D Technology: The pivot to more visually immersive real-time 3D mobile gaming content is at the heart of the Immortalis GPU. This technology brings ray tracing and variable rate shading to deliver more realistic mobile real-time 3D experiences. 
• Security and AI: Arm’s built-in technology features for security and AI are essential for the ongoing development of next-generation XR devices. 
• Strong software foundations: In many instances, companies that are developing XR applications are using versions of the Android Open Source Project as their base software. This ensures that they benefit from years of software investment from Arm, allowing their software enablement efforts to scale across a wide range of XR devices. 

Through the Arm Compute Platform, we are providing a performant, efficient, secure, and highly accessible solution with advanced AI capabilities that meet the needs of XR devices and experiences, now and in the future. Recent technology developments have shown that mainstream XR could be coming soon, with Arm’s technologies ideally placed to deliver truly immersive experiences for this next future of computing.  

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As a developer, you know how crucial it is to build applications that scale efficiently while keeping costs down. As the cloud landscape evolves, so does the technology running behind the scenes. In recent years, more and more companies are discovering the advantages of migrating their applications from x86-based architectures to Arm. With significant performance gains and lower total costs of ownership, Arm is quickly becoming the go-to architecture for companies looking to future-proof their workloads. 

Discover how to easily migrate your applications to Arm for better performance and cost savings. Our Arm Migration Guide will help you ensure a smooth transition for containerized workloads, cloud-managed services, and Linux applications. 

The Power of Arm: Performance and Efficiency

Arm processors, like those in AWS Graviton, Google Axion, and Microsoft Azure’s Ampere-based offerings, are designed to deliver superior performance at lower costs. With up to 60% energy savings and a 50% boost in performance, migrating to Arm-based cloud instances opens new opportunities for developers looking to optimize their workloads. Arm also offers a higher density of cores, which translates into improved scalability and the ability to handle more tasks simultaneously.

Moreover, Arm’s architecture is designed with flexibility in mind, allowing you to future-proof your development. Once you migrate your workloads to Arm, they are compatible across multiple cloud providers, giving you the agility to scale your applications on any Arm-based cloud platform, including AWS, Google Cloud, and Microsoft Azure.

Port to Arm Once, Open up our Entire Cloud Ecosystem and Workflows

 The growing adoption of Arm-based solutions by major cloud providers has spurred increased software compatibility and optimization, making it easier for developers to leverage Arm’s strengths. For AI workloads specifically, Arm’s focus on specialized processing elements and heterogeneous computing allows for efficient execution of machine learning algorithms. This combination of power efficiency, scalability, and AI acceleration capabilities positions the Arm ecosystem as a compelling choice for organizations looking to optimize their cloud infrastructure and AI applications.

Customer Success on Arm 

Honeycomb.io and FusionAuth both demonstrate how easy and beneficial it is to migrate to an Arm-based infrastructure.

• Honeycomb.io Reduces Infrastructure Costs by 50%
  A leader in the observability space, Honeycomb transitioned from a legacy architecture to Arm-based AWS Graviton processors to handle its massive data processing needs. The results were immediate and striking. Honeycomb realized a 50% reduction in infrastructure costs while maintaining high performance and using fewer instances. This migration allowed Honeycomb to focus on what they do best—providing deep insights into system behavior—without worrying about spiraling infrastructure costs.
  
• FusionAuth Increases Logins Per Second up to 49%
  Migrating to Arm wasn’t just an experiment—it was a breakthrough. After load testing on Arm-based AWS Graviton instances, FusionAuth saw a 26% to 49% increase in logins per second compared to legacy systems. A seamless transition, the company also achieved 8% to 10% cost savings along the way. FusionAuth now runs the majority of its cloud infrastructure on Arm-based instances, enabling them to support a wide range of use cases from IoT to high-performance cloud platforms.

The Path to Migration: It’s Easier Than You Think

Migrating from legacy architectures to Arm is a smooth process that doesn’t require a complete code overhaul. Companies like Honeycomb and FusionAuth successfully made the transition using Arm’s strong ecosystem of developer tools and support for adapting code, testing, debugging, and optimizing performance. Whether you’re running Java, Golang, or other popular languages, Arm provides compatibility with your existing tech stack. The flexibility of Arm’s architecture ensures that your applications perform better with fewer resource demands, leading to improved price-performance ratios.

Developers should start by assessing their current software stack, including operating systems, programming languages, development tools, and dependencies. Next, they should set up a development environment that supports Arm architecture, which can be done using emulation, remote hardware, or physical Arm hardware. The migration process typically involves recompiling applications written in compiled languages like C/C++, Go, and Rust, while interpreted languages such as Python, Java, and Node.js may require minimal changes.

Developers should also ensure that all necessary libraries and dependencies are available for Arm. Testing and validation are crucial steps to identify and resolve any compatibility issues. Finally, developers can deploy their Arm-compatible workloads to cloud platforms like AWS, Google Cloud, and Microsoft Azure, which offer robust support for Arm-based instances. 

Whether you are working on battery-powered devices, embedded systems, or IoT applications, migrating to Arm is a strategic decision that provides cost savings, superior performance, and sustainability. Developers around the world are choosing Arm to build more reliable, scalable, and power efficient applications. 

The post Why Developers are Migrating to Arm appeared first on Arm Newsroom.

In the rapidly evolving landscape of cloud computing and AI, businesses need ways to optimize performance, reduce costs and stay ahead of the competition. Enter Arm Neoverse – a game-changing architecture that’s reshaping the future of AI data centers and AI infrastructure.

Arm Neoverse has emerged as the go-to choice for industry leaders looking to drive innovation while minimizing total cost of ownership (TCO) in their AI data centers. With its unmatched performance, scalability, and power efficiency, Arm Neoverse is redefining what’s possible in modern computing environments.

Cloud Giants Lead the Way

It’s not just hype – major cloud service providers are already harnessing the power of Arm Neoverse:

• Amazon Web Services (AWS) with Graviton
• Microsoft with Cobalt
• Google with Axion

These trail-blazers recognize that Arm Neoverse delivers the critical performance and efficiency needed to meet today’s most demanding workloads. But the benefits of Arm aren’t limited to the cloud. 

Bringing Arm to Your Data Center

Enterprise customers can now leverage Arm technology on-premises, thanks to trusted OEM partners like HPE and Supermicro. As AI capabilities become increasingly central to enterprise applications, Arm-based solutions offer a path to consolidate outdated x86 servers onto fewer, high-performance, power-efficient machines.

We understand that considering and adopting new technology can seem daunting. That’s why we’ve created a comprehensive guide to help you navigate your Arm Neoverse journey. Our guide addresses common concerns, showcases real-world success stories, and provides a clear path forward for enterprises looking to harness the power of Arm for their AI data centers.

Click to read Accelerate Your AI Data Center Dreams

Ready to accelerate your AI data center dreams? Learn how Arm Neoverse can transform your infrastructure and propel your business into the future of AI-driven computing.

Don’t just keep pace – lead the AI revolution with Arm Neoverse.

The post How Arm Neoverse can Accelerate Your AI Data Center Dreams appeared first on Arm Newsroom.

For AI, no individual piece of hardware or computing component will be the “one size fits-all” solution for all workloads. AI needs to be distributed across the entire modern topography of computing, from cloud to edge – and that requires a heterogeneous computing platform that offers the flexibility to use different computational engines, including the CPU, GPU and NPU, for different AI use cases and demands.

The Arm CPU already provides a foundation for accelerated AI everywhere, from the smallest embedded device to the largest datacenter. This is due to its performance and efficiency capabilities, pervasiveness, ease of programmability and flexibility.

Focusing on flexibility, there are three key reasons why this is hugely beneficial to the ecosystem. Firstly, it means the Arm CPU can process a broad range of AI inference use cases, many of which are commonly used across billions of devices, like today’s smartphones, and in cloud and data centers worldwide  – and not only that, because beyond inference the CPU is often used for additional tasks in the stack, such as data pre-processing and orchestration. Secondly, developers can run a broader range of software in a greater variety of data formats without needing to build multiple versions of the code. And, thirdly, CPU’s flexibility makes it the perfect partner for accelerated AI workloads.

Delivering diversity and choice to enable the industry to deploy AI compute their way

Alongside the CPU portfolio, the Arm compute platform includes AI accelerator technologies, such as GPUs and NPUs, which are being integrated with the CPU across various markets.

In mobile, Arm Compute Subsystems (CSS) for Client features the Armv9.2 CPU cluster integrated with the Arm Immortalis-G925 GPU to offer acceleration capabilities for various AI use cases, including image segmentation, object detection, natural language processing, and speech-to-text. In IoT, the Arm Ethos-U85 NPU is designed to run with Cortex-A-based systems that require accelerated AI performance, such as factory automation.

Also, in addition to Arm’s own accelerator technologies, our CPUs give our partners the flexibility to create their own customized, differentiated silicon solutions. For example, NVIDIA’s Grace Blackwell and Grace Hopper superchips for AI-based infrastructure both incorporate Arm CPUs alongside NVIDIA’s AI accelerator technologies to deliver significant uplifts in AI performance.

The Grace Blackwell superchip combines NVIDIA’s Blackwell GPU architecture with the Arm Neoverse-based Grace CPU. Arm’s unique offering enabled NVIDIA to make system-level design optimizations, reducing energy consumption by 25 times and providing a 30 times increase in performance per GPU compared to NVIDIA H100 GPUs. Specifically, NVIDIA was able to implement their own high-bandwidth NVLink interconnect technology, improving data bandwidth and latency between the CPU, GPU and memory – an optimization made possible thanks to the flexibility of the Arm Neoverse platform.

Click to read Accelerate Your AI Data Center Dreams

Arm is committed to bringing these AI acceleration opportunities across the ecosystem through Arm Total Design. The program provides faster access to Arm’s CSS technology, unlocking hardware and software advancements to drive AI and silicon innovation and enabling the quicker development and deployment of AI-optimized silicon solutions.

The Arm architecture: Delivering the unique flexibility AI demands

Central to the flexibility of the Arm CPU designs is our industry-leading architecture. It offers a foundational platform that can be closely integrated with AI accelerator technologies and supports various vector lengths, from 128 bit to 2048 bit, which allows for multiple neural networks to be executed easily across many different data points.

The flexibility of the Arm’s architecture enables diverse customization opportunities for the entire silicon ecosystem, with our heritage built on enabling partners to build their own differentiated silicon solutions as quickly as possible. This unique flexibility also allows Arm to continuously innovate the architecture, introducing critical instructions and features on a regular cadence that accelerate AI computation to benefit the entire ecosystem, from leading silicon partners to the 20 million plus software developers building on the Arm compute platform.  

This started with the Armv7 architecture, which introduced advanced Single Instruction Multiple Data (SIMD) extensions, such as NEON technology, as Arm’s initial venture into machine learning (ML) workloads. It has been enhanced over the past few years, with additions focused on vector dot product and matrix multiplication as part of Armv8, before the introduction of Arm Scalable Vector Extensions 2 (SVE2) and the new Arm Scalable Matrix Extension (SME) as key elements of Armv9 that drive higher compute performance and reduced power consumption for a range of generative AI workloads and use cases.

Seamless integration with AI accelerator technologies

Arm is the compute platform for the age of AI, driving ongoing architectural innovation that directly corresponds with the evolution of AI-based applications that are becoming faster, more interactive, and more immersive. The Arm CPU can be seamlessly augmented and integrated with AI accelerator technologies, such as GPUs and NPUs, as part of a flexible heterogeneous computing approach to AI workloads.

While the Arm CPU is the practical choice for processing many AI inference workloads, its flexibility means it is the perfect companion for accelerator technologies where more powerful and performant AI is needed to deliver certain use cases and computation demands. For our technology partners, this helps to deliver endless customization options to enable them to build complete silicon solutions for their AI workloads.

The post Why Arm is the Compute Platform for All AI Workloads appeared first on Arm Newsroom.

September 2024 has been another innovative month for us, showcasing various Arm innovations across various domains. From enhancing CPU performance and efficiency to revolutionizing AI software development and optimizing automotive microcontrollers, we continue to push the boundaries of technology and ensure the future of computing is built on Arm.

Here’s a summary of the top 10 technological developments at Arm this month:

Setting new performance and efficiency standards with Armv9 CPUs and SVE2

Each new generation of Arm CPUs gets faster and better, meeting the needs of modern computing tasks. Poulomi Dasgupta, Senior Manager of Consumer Computing, highlights how Armv9 CPUs and their exclusive SVE2 optimizations are part of the latest Arm technological advancements. They help enhance performance and efficiency for mobile devices and boosts HDR video decoding by 10% and image processing by 20%. This helps improve battery life and app performance for popular apps like YouTube and Netflix.

Likewise, Yibo Cai, Principal Software Engineer, explains how the SVMATCH instruction introduced with SVE2 speeds up multi-token searches, simplifying tasks like parsing CSV files. This further helps reduce the number of operations needed, leading to better performance, as seen in the optimized Sonic JSON decoder. A highlight of SVMATCH is that it helps enhance various software engineering tasks, making data processing faster and more efficient.

How Arm and Meta are transforming AI software development

Sy Choudhury, Director, AI Partnerships at Meta, explains how Arm and Meta are accelerating AI software development through open innovation and optimizing large language models (LLMs), like Llama, across data centers, smartphones, and IoT devices.

Learn more about the latest Kleidi integrations for PyTorch, ExecuTorch, and more, as well as how to unlock the true performance potential of LLMs with Arm’s cutting-edge innovations – all while simplifying model customization and deployment.

Faster PyTorch Inference using Kleidi on Arm Neoverse

PyTorch is a popular open-source library for machine learning. Ashok Bhat, Senior Product Manager, explains how Arm has improved PyTorch’s inference performance using Kleidi technology, integrated into the Arm Compute Library and KleidiAI library. This includes optimized kernels for machine learning (ML) tasks on Arm Neoverse CPUs.

These optimizations lead to significant performance improvements. For example, using torch.compile can achieve up to 2x better performance compared to Eager mode for various models. Additionally, new INT4 and INT8 kernels can enhance inference performance by up to 18x for specific models like Llama and Gemma. These advancements make PyTorch more efficient on Arm hardware, potentially reducing costs and energy consumption for machine learning tasks.

Advancing ASR Technology with Kleidi on Arm Neoverse N2

Automatic Speech Recognition (ASR) technology is widely used in applications like voice assistants, transcription services, call center analytics, and speech-to-text translation. Willen Yang, Senior Product Manager, and Fred Jin, Senior Software Engineer, introduce FunASR, an advanced toolkit developed by Alibaba DAMO Academy.

FunASR supports both CPU and GPU, with a focus on efficient performance on Arm Neoverse N2 CPUs. It excels in accurately understanding various accents and speaking styles. Using bfloat16 fastmath kernels on Arm Neoverse N2 CPUs, FunASR achieves up to 2.4 times better performance compared to other platforms, making it a cost-effective solution for real-world deployments.

Optimizing LLMs with Arm’s Kleidi Innovation

As Generative AI (GenAI) transforms business productivity, enterprises are integrating Large Language Models (LLMs) into their applications on both cloud and edge. Nobel Chowdary Mandepudi, Graduate Solutions Engineer, discusses how Arm’s Kleidi technology enhances PyTorch for running LLMs on Arm-based processors. This integration simplifies access to Kleidi technology within PyTorch, boosting performance.

The demo application demonstrates significant improvements, such as faster token generation and reduced costs. For example, the time to generate the first token is less than 1 second, and the decode rate is 33 tokens per second, meeting industry standards for interactive chatbots.

These optimizations make running LLMs on CPUs practical and effective for real-time applications like chatbots, leading to more efficient and cost-effective AI solutions. This benefits businesses and developers by reducing latency and operational costs.

Demonstrating AI performance uplifts with KleidiAI

Arm’s KleidiAI, integrated with ExecuTorch, enhances AI inference on edge devices. In a demo by Gian Marco Iodice, real-time inference of the Llama 3.1 8B parameter model is showcased on a mobile phone. This demo highlights KleidiAI’s capability to accelerate various AI models across billions of Arm-based devices globally.

KleidiAI uses optimized micro-kernels and advanced Arm CPU instructions like SMMLA and FMLA to deliver efficient AI performance without compromising speed or accuracy.

Meanwhile, Nobel Chowdary Mandepudi demonstrates how KleidiAI boosts AI performance in the cloud using AWS Graviton 4 instances. This demo highlights KleidiAI’s ability to drive AI performance on Arm-powered cloud servers while maintaining energy efficiency.

This side-by-side demo compares PyTorch inference with and without KleidiAI optimizations, showcasing significant improvements in efficiency and speed with the Llama 3.1 8B model. KleidiAI leverages advanced Arm instructions to accelerate generative AI workloads, enhancing text generation and prompt evaluation.

Arm’s growing ecosystem and server integration

The Arm ecosystem is expanding rapidly across all sectors, including Microsoft Copilot+ PCs, cloud services (AWS, Google, Microsoft), and automotive innovations like in-vehicle infotainment (IVI) and advanced driver assistance systems (ADAS). Steve Demski, Director of Product Marketing, highlights the integration of several hundred HPE ProLiant RL300 Gen11 servers, powered by Ampere Altra Max CPUs, into Arm’s Austin datacenter.

These high-performance, power-efficient servers support various workloads and align with Arm’s goal to run at least 50% of their on-premises EDA cluster infrastructure on Arm by 2024. This transition boosts productivity frees up space and power for future workloads like generative AI, and offers a lower cost-per-core, enabling more efficient budget allocation.

Simplifying automotive microcontrollers with EB tresos Embedded Hypervisor

The EB tresos Embedded Hypervisor by Elektrobit allows multiple virtual machines (VMs) to run on a single automotive microcontroller, supporting various operating systems and applications. Dr. Bruno Kleinert from Elektrobit explains how this technology optimizes resource use, enhances safety, and cuts costs.

The hypervisor technology is crucial for software-defined vehicles (SDVs), enabling flexible updates to vehicle functions. It also supports sustainable design by reducing hardware, cabling, weight, and energy use, with the technology touted to be ready for mass production in October 2024, and safety-approved versions expected in early 2025.

Boosting performance for AI and beyond with OpenRNG

OpenRNG is an open-source Random Number Generator (RNG) library that boosts performance for AI, scientific, and financial applications. Kevin Mooney, Staff Software Engineer, explains how it can replace Intel’s Vector Statistics Library (VSL) and supports various random number generators, including pseudorandom, quasirandom, and true random generators.

OpenRNG significantly improves performance, enhancing PyTorch’s dropout layer by up to 44 times and speeding up the C++ standard library by 2.7 times. OpenRNG is crucial for applications needing fast and reliable random number generation, like AI, gaming, and financial modeling, ensuring consistent results across systems.

Pioneering automotive safety with Arm Software Test Libraries

Integrating Arm’s Software Test Libraries (STLs) into automotive systems boosts safety and reliability, meeting ISO26262 standards. Andrew Coombes, Principal Automotive Software Product Manager, and ETAS explains how Arm STL can be used with Classic AUTOSAR to improve diagnostics, detect faults early, and offer flexible integration. Using Arm STLs with microcontroller hypervisors based on Arm architecture supports mixed-criticality systems and enhances fault mitigation. Meanwhile, achieving Functional Safety certification requires comprehensive strategies, as detailed in a joint white paper by Exida and Arm.

Optimizing video calls with artificial intelligence

While video conferencing is a ubiquitous tool for communication, it is not always a straightforward plug-and-play experience, as adjustments may be needed to ensure a good audio and video setup. Ayaan Masood, a Graduate Engineer, has developed a demo mobile app that uses a neural network model to improve video lighting in low-light conditions.

This app processes video frames in real time, providing smooth and clear visuals. It ensures a professional appearance during video calls, which is essential for remote work and social interactions. The success of this app highlights the potential of AI to solve everyday problems, paving the way for more AI-driven solutions in various fields.

The post Beyond the Newsroom: 10 Latest Innovations from Arm in September 2024 appeared first on Arm Newsroom.

Frederique Olivier, a dedicated wildlife and landscape cinematographer, is on a mission to inspire people by highlighting the beauty of the natural world. Traveling to fascinating, often remote locations, she uses the latest camera technology to painstakingly bring to life stunning wildlife images. Her time-lapse work captures natural phenomena, like the subtle movement of ice and the ever changing sky, and her aerial work follows the behavior of exotic animals such as polar bears and bowhead whales, bringing these images to those who may never experience them firsthand. Through her lens, Frederique helps others gain a deeper appreciation of the environment, fostering a connection that she hopes will inspire greater respect and preservation.

The tech powering Frederique’s latest expedition 

Frederique’s recent expedition to Baffin Island brought her face to face with some of the most remote and extreme conditions on Earth. As temperatures ranged from minus 10 °C to minus 25 °C, she endured furious wind shifts that nearly blew away the camp. To maintain productivity in such weather, the camera tools and technologies she requires need to not only survive the expedition but continue to function efficiently throughout it. That’s why she relies on cutting-edge technology for both logistics—safety, communication, and navigation—as well as cinematography.

Equipped with a drone for aerial 4K UHD footage, a mirrorless digital camera, a GoPro pole cam, and a suite of high-tech navigation and messaging tools, Frederique captures stunning footage in the harshest conditions. She also uses portable wireless hard drives with built-in batteries to back up footage in the field, protecting her work from potential loss. These tools are lighter, more energy efficient, and more reliable than ever, enabling her to push the boundaries of what’s possible in wildlife cinematography while driving next-generation experiences on Arm technology.

During expeditions, the technology Frederique uses is not just about capturing and storing images; it’s also about ensuring safety and maintaining communication with the outside world. Depending on the filming location, she sometimes journeyed 15 to 20 kilometers in a day. Satellite-based messaging and navigation devices like the inReach and Iridium Go allowed her to stay connected through her smartphone, ensuring peace of mind even in the most remote spots.

Throughout her journey, many of the devices she depends on are powered by Arm. 

How do Arm processors support wildlife cinematography?

Known for the world’s most power-efficient CPUs, Arm processors deliver high performance, enabling next-generation AI and smart devices, from tiny sensors to smartphones, cameras, and data centers. In extreme environments, Arm’s energy-efficient compute technology is critical to Frederique’s work allowing her to efficiently and productively ply her craft. In cold conditions, the battery life of drones and cameras can diminish quickly so there’s a critical need for a more power-efficient processor to support battery powered devices. Sourcing power can be a challenge, as solar panels could not always be used due to mountain shadows and a generator was required to charge the drone. This highlights the importance of energy efficient tools to minimize power needs.

Designed to improve data processing speeds, Arm advancements in performance, power efficiency and AI capabilities, help Frederique’s equipment—from cameras to communications, along with her drone and smartphone—perform complex tasks, such as stabilizing images, enhancing picture quality, and processing data in real-time. The lightweight, high-performance nature of Arm-powered devices means she can carry the needed tools without being weighed down or at the mercy of limited power or availability. Rather than worrying about whether her devices will work, she can focus on finding the best shooting locations.

Enhancing Frederique’s ability to shoot breathtaking images with greater efficiency, Arm processors power the AI elements of her cameras. These technologies not only improve overall image quality but can adapt to varying lighting and environmental conditions, a key component to shooting successfully in the wild. AI can even sift through hours of digital footage to quickly and accurately analyze the movement patterns of specific animals, so she can take advantage of more optimal shooting locations. These features have heightened her appreciation of how much data an Arm processor can handle, along with its ability to power her equipment more efficiently.

Powering tomorrow’s expeditions with power-efficient Arm processors

As Frederique continues to explore new frontiers in wildlife cinematography, Arm processors remain a key enabler of her equipment and work. Helping her bring the wonders of the wild to audiences around the globe, the Arm-powered technology powers and amplifies her mission through the challenges of each expedition. Sharing her unique perspective with the world, she not only documents the incredible, often unseen beauty of nature but also inspires others to appreciate and protect it.

Arm technology powers human achievement, enabling people with purpose—like Frederique—to accomplish extraordinary things. As technology evolves, Arm is at its core, driving innovation so we can all witness more remarkable achievements. To learn how other extraordinary people are using Arm technology, visit Purpose on Arm, dedicated to showcasing others who are finding their purpose on Arm.

The post Capturing the Wild: How Arm Technology Fuels Frederique Olivier’s Cinematography Expeditions appeared first on Arm Newsroom.

How does a car make split-second decisions such as switching between real-time traffic updates, adaptive braking, or lane assist correctional steering? There is a growing need for such techniques that allow cars to operate autonomously while managing dynamic, control, and safety challenges. This becomes more paramount amidst the growing demand for safer, smarter, and more connected vehicles and the increasing adoption of autonomous driving features. 

What is Arm Split, Lock, and Hybrid Mode?

Automotive systems like Advanced Driver Assistance Systems (ADAS), Automated Driving Systems (ADS), and In-vehicle Infotainment (IVI) need to process large volumes of data quickly all while maintaining multiple levels of safety integrity. Balancing these requirements is crucial for vehicles, particularly with ongoing computing challenges around performance, power, and area.

Designed to make tomorrow’s driving exhilarating, safe, and convenient, Arm’s Split, Lock, and Hybrid processing modes offer the versatility needed to support various automotive safety levels, further enabling automakers to develop vehicles that are safe, powerful, and adaptable.  

Arm’s Split, Lock, and Hybrid modes offer a comprehensive solution by enabling a single silicon design to operate flexibly in different modes tailored to specific safety and performance needs. This versatility allows automakers and Tier 1 suppliers to deploy the same hardware across a wide range of safety-critical automotive use cases. 

Split, Lock, and Hybrid Modes Use cases and examples

Split Mode: Maximizing Performance

Use case example: A vehicle’s IVI system handles many non-safety critical tasks, such as playing music, providing navigation directions, and managing cabin temperature, all while maintaining a seamless driver experience.  

How Split Mode Delivers: In Split mode, processor cores operate independently, delivering maximum performance when handling demanding applications. This enables high throughput in applications where rapid response and high data processing are critical, such as running multimedia, navigation, communication features, high-end graphics, and fast processing. This mode is perfect for scenarios where speed and efficiency are crucial, and safety isn’t the primary concern. 

Lock Mode: Uncompromising Safety: ASIL D 

Use case example: Various ADAS features are used when driving through dense fog on a busy highway. The vehicle will actively scan the environment, anticipate potential hazards, deploy traction control, and assist with steering and even brakes.  

The vehicle system processes obstruction data, calculates risks, and automatically steers the driver away from the hazard, or applies emergency braking, protecting them from a potential accident. It is imperative that these systems are fail-safe proof in life-threatening situations.  

How Lock Mode Ensures Safety: Lock mode is engineered for the most stringent safety-critical applications, such as ADAS L2+ features, where system failure could have life-threatening consequences. In this mode, processor cores operate in pairs, with the Arm DynamIQ Shared Unit (DSU) logic and memory operating in lockstep, ensuring a redundant operation that enables fail-safe execution. This redundancy is essential for systems requiring the highest safety standards, like ASIL D/SIL 3, which govern critical functions such as automatic braking and collision avoidance and acts as a countermeasure for security threat. 

Hybrid Mode: The Balanced Solution for ASIL B 

Use case example: Hybrid mode works harmoniously to optimize power consumption while maintaining essential safety measures, ensuring a smooth driving experience without compromising reliability or control. In moderate-risk scenarios, such as maintaining a safe distance from other vehicles using adaptive cruise control or managing energy efficiently, hybrid mode ensures that critical functions operate harmoniously to enhance your driving experience without unnecessary power consumption or safety compromises. 

How Hybrid Mode Balances: Hybrid mode is engineered with balanced performance and safety in mind. In this mode, cores operate independently, and the DSU logic operates in lockstep. This allows for some redundancy and safety features while maintaining a higher level of performance and efficiency compared to full lockstep. For mid-tier safety features such as lane departure warnings or energy management in electric vehicles (EVs) that only need ASIL B/SIL 2, Hybrid mode coupled with Software test libraries (STLs) provides a balance between Availability, safety, and performance.  

Paving the Way for Automotive Excellence

Arm’s Split, Lock, and Hybrid modes are more than just a technical term—they are the key to unlocking the future of automotive innovation. By offering flexible, high-performance, and safety-conscious solutions, Arm is the best choice of foundational platform to build the future of automotive with safe and reliable vehicles.

The post Pioneering Automotive Safety with Arm Split, Lock, and Hybrid Modes appeared first on Arm Newsroom.

As Arm CEO Rene Haas said on the day of our initial public offering (IPO) one year ago “an IPO is just a moment in time”, with plenty of opportunities ahead to build the future of computing on Arm. Since September 14, 2023, we have moved at pace to fulfil this mission.

Across the entire stack from the foundational technology to the software, Arm has had a profound impact in the past year as a public company. This includes Arm Compute Subsystems (CSS) for multiple markets, the growing influence and adoption of Armv9, new Arm-powered silicon and technologies, rising software capabilities and various announcements and initiatives that showcase our leading role in AI and the global technology ecosystem.

However, this is just the start of our journey as a public company, with plenty of exciting new developments and growth opportunities in the future. This is made possible by our high performance, power-efficient technologies for the age of AI.

Check out our “One Year After the IPO” report below that provides more details about these momentous achievements during the past year.

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