Energy efficient deep neural network inference hardware for consumer devices

Paul Whatmough, Arm Research

Paul Whatmough received the Doctorate degree from University College London, UK. He was previously with Philips, NXP, Arm, and Harvard, working in the areas of ML, DSP, wireless, accelerators, and circuits. Currently, he leads research at Arm ML Research Lab Boston, and is an Associate at Harvard.

Deep neural networks (DNNs) have quickly become an essential workload across computing form factors, including IoT, mobile, automotive and datacenter. However, DNN inference demands an enormous number of arithmetic operations and a large memory footprint. In this talk, we will explore the co-design of DNN models and hardware to achieve state-of-the-art performance for real-time, energy-constrained inference applications.

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Energy efficient deep neural network inference hardware for consumer devices

Speaker

Abstract