Arm has introduced its Armv9 Edge AI platform, featuring the new Arm Cortex-A320 CPU and the AI accelerator for Edge AI, Arm Ethos-U85 NPU, enabling AI models of over one billion parameters to run on device.
OEMs are under greater pressure to deliver solutions faster than ever before. They need to meet the growing computing demands across IoT applications, such as autonomous vehicles navigating factory floors, smart cameras that can adapt their functionality through software updates, and human-machine interfaces that offer more natural, AI-driven interactions.
To innovate and scale at pace, they require the flexibility to execute their AI workloads where it makes sense, more robust security and increased software flexibility – Armv9 technology delivers this at scale. Arm’s new platform brings together a brand-new ultra-efficient Armv9 CPU, Cortex-A320, along with the Ethos-U85 NPU with operator support for transformer networks, reportedly creating the world’s first Armv9 edge AI platform optimised for IoT. The platform delivers an 8x improvement in machine learning (ML) performance compared to the Cortex-M85-based platform it launched last year.
Arm’s partners can now deploy the power of Armv9 technology across the spectrum of computing, from Cloud to Edge. From silicon partners licensing this technology to build SoCs, to ODMs and OEMs building their next generation devices, this development has been welcomed by partners across the industry which include AWS, Siemens, Renesas, Advantech and Eurotech.
The launch of this new Edge AI platform marks a milestone in the evolution of Edge computing. The Cortex-A320 brings advanced AI capabilities and developer benefits to IoT, extending the features of the Armv9 architecture to power efficient devices, alongside comprehensive software support.
Cortex-A320 takes advantage of Armv9 architectural features, such as SVE2 for ML performance, and delivers a 10x ML performance uplift and 30 percent scalar performance uplift compared to its predecessor, Cortex-A35.
The platform’s Armv9.2 architecture also brings advanced security features like Pointer Authentication (PAC), Branch Target Identification (BTI) and Memory Tagging Extension (MTE) to even the smallest Cortex-A devices. This is essential as Edge devices often operate in exposed environments and handle sensitive data.
One of the most significant barriers to Edge AI adoption has been the complexity of software development and deployment. This is where the platform’s software ecosystem comes into its own. Arm announced extending Arm Kleidi to IoT – a set of compute libraries for developers of AI frameworks designed to optimise AI and ML workloads on Arm-based CPUs with no additional developer work needed.
KleidiAI is already integrated into popular IoT AI frameworks, such as Llama.cpp and ExecuTorch or LiteRT via XNNPACK, accelerating the performance of key models, including Meta Llama 3 and Phi-3. For example, Kleidi AI brings up to 70% more performance to the new Cortex-A320 when running Microsoft’s Tiny Stories dataset on Llama.cpp.
This matters because in today’s fast-paced technology landscape, time-to-market can make or break the success of a product. The new platform also maintains software compatibility with higher-performance Cortex-A processors. This scalability ensures that developers can build solutions that grow and adapt as requirements change. With access to the vast Armv9 ecosystem and compatibility with both rich operating systems such as Linux, and real-time operating systems such as Zephyr, developers have significant flexibility, can leverage existing tools and knowledge, and take advantage of software reuse, reducing time to market and lowering total cost of ownership.
“The evolution of Edge AI is accelerating, and advancements in Arm’s IoT computing architecture will bring new possibilities for intelligence at the Edge,” said Miller Chang, President of Embedded Sector, Advantech. “As a leading player in Edge Computing and Edge AI, Advantech sees this innovation as a significant step forward for the broader Arm ecosystem, enabling smarter, more efficient, and secure AI-driven applications across industries. This innovation will drive industry growth and technological breakthroughs in the edge computing market.”
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