Industry Expert Blogs

Optimizations achieve up to 4X increase in inference throughput with 3X memory reduction

The proliferation of AI at the edge offers several advantages including decreased latency, enhanced privacy, and cost-efficiency. Arm has been at the forefront of this development, with a focus on delivering advanced AI capabilities at the edge across its Cortex-A and Cortex-M CPUs and Ethos-U NPUs. However, this space continues to expand rapidly, presenting challenges for developers looking to enable easy deployment on billions of edge devices.

One such challenge is to develop deep learning models for edge devices, since developers need to work with limited resources such as storage, memory and computing power, and still balance good model accuracy and run-time metrics such as latency or frame rate. An off-the-shelf model designed for a more powerful platform may be slow or not running at all when deployed on a more resource-constraint platform.

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