Industry Expert Blogs

Arm launches the highest performing, most energy efficient Ethos NPU to date for IoT markets.

As artificial intelligence (AI) continues to have more influence and impact in our day-to-day lives, the domain is migrating from cloud-based inferencing to edge and endpoint inferencing. Edge-based inferencing brings intelligence across a broad range of IoT devices, enabling data to be processed locally and decisions to be made in real time with increased data privacy and security.

Arm’s Ethos NPUs

Arm has been developing edge AI accelerators to support the growing need of edge and endpoint inferencing workloads for several years. Through Arm’s Ethos-U55 and Ethos-U65 NPUs, we have two very successful products bringing high performance, energy efficient solutions for AI applications at the edge and endpoints.

Ethos-U55 is deployed in many Cortex-M based heterogenous systems. The Ethos-U65 extends the applicability of the Ethos-U family to Cortex-A-based systems, while delivering twice the on-device machine learning (ML) performance. Both products offer a unified toolchain for easy development and support for common ML network operations, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

The influence of transformer architecture

Introduced in 2017, transformer architecture has revolutionised generative AI and become the architecture of choice for many new neural networks. Transformer-based models can process sequential data using attention mechanisms and have achieved state-of-the-art results in many AI tasks, such as machine translation, natural language understanding, speech recognition, segmentation and image captioning.

These models can be adapted and compressed to run efficiently on edge devices without compromising much on accuracy and showcasing state-of-the-art advancements across many edge and endpoint use cases.

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