CNS Core: Small: Importance-Aware Compressive Inference for Efficient Embedded Vision

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

This project explores the concept of Compressive Inference in the context of energy-efficient and low-latency machine learning and artificial intelligence (AI) applications. Compressive Inference is the biologically inspired idea of using highly heterogeneous, multi-round,feedback-controlled sampling and analysis of signals to minimize latency and energy consumption while maximizing inference accuracy, which stands in contrast to the commonly optimized but often less relevant objective of signal reconstruction accuracy. Methods of restructuring and compressing knowledge representations to improve efficiency are also being explored. The project focuses on applications and systems facing tight energy consumption and latency constraints, namely computer vision applications running on low-power embedded systems, although many of the ideas developed will have application in other domains, e.g., datacenter-based machine learning and AI applications. Based on preliminary results, it is likely that the project will enable order-of-magnitude improvements in machine learning and AI application inference latencies and energy consumptions, thereby enabling the deployment of sophisticated analysis techniques in applications where they were previously impractical, e.g., low-cost home security systems and agricultural sensing applications, as well as applications where sophisticated analysis was detrimentally resource and power hungry, e.g., autonomous driving and wearable vision-based assistants. The resulting improvement in efficiency will enable local, on-device learning, thereby making it possible for machine learning and AI systems to adapt to their environments, thereby reducing the tendency to perform poorly on data dissimilar to samples in centralized training datasets. The project also has an educational component, in which students with a broad range of backgrounds will learn about state-of-the-art approaches to machine learning, AI, and embedded system design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.