Co-Designing Hardware and Models for Efficient On-Device ML Inference

Deep learning inference at the edge continues to deliver state of the art results on real-world applications involving images, video, speech, and human activity. The workhorse behind these advances—increasingly complex neural network models—continue to grow in size and computational requirements. These advances place significant demand on the energy-constrained hardware platforms responsible for executing such models and are driving trends like low-precision number formats, network pruning, and complexity-reducing network transforms. This talk will discuss emerging research aimed at co-designing neural networks and hardware to enable even larger, more complex models to operate on highly-constrained hardware platforms.