Industry Expert Blogs

The innovation that lies ahead on the road to a trillion connected devices are nothing short of astonishing, as we push compute and artificial intelligence (AI) and machine learning (ML) capabilities from the cloud to the edge. To get there, engineers are rethinking how to push the boundaries of design around device size, reliability, and efficiency. In the process, they are looking to new technology nodes and optimized IP to wring the most out of their designs to ensure success at the edge and endpoints.

Nowhere is this transformation more vital than in edge computing, where most edge devices are small, ultra-low power and cost-constrained. Specialized and hyper-efficient AI compute has led to the design of Arm Ethos-N78 NPU that is highly task-specific and is designed for faster execution of AI applications. Specifically, the N78 NPU is lightning quick at performing the vector and matrix computations typical with ML workloads. Whether pairing Ethos-N78 with Cortex-A75 or the newly introduced Cortex-A78, the legacy and familiarity enables designers to reduce their time to market and improve efficiency to handle general compute functions.

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