Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices

Abstract

Approximate computing (AxC) has advanced from being an emerging design paradigm to becoming one of the most popular and effective methods of energy optimization for applications in the domains of computer vision, image/video processing, data mining, analytics, and search. The simultaneous rise of artificial intelligence (AI) has provided an additional thrust to the adoption of various AxC techniques in intelligent edge platforms where energy-efficiency is not only desirable but necessary. In spite of the big rise in interest for AxC, the adoption of approximate hardware has mostly been limited to only one component of the system (usually the processing subsystem) which often contributes only a fraction of the overall system-level power. A full system approach to AxC enables us to extend approximations to other subsystems, such as the memory, sensor, and communications subsystems. This paper presents the foundational concepts of an approximate TinyML system that applies approximations synergistically to multiple subsystems in an edge inference device. These approximations are applied intelligently to significantly reduce energy while incurring a negligible loss in application-level quality. We demonstrate multiple versions of an approximate smart camera system that can execute state-of-the-art deep neural networks (DNNs) while consuming only a fraction of the total energy in a typical system.