An Energy-Efficient Heterogeneous System for Embedded Learning and Classification

Abstract

Embedded learning applications in automobiles, surveillance, robotics, and defense are computationally intensive, and process large amounts of real-time data. Systems for such workloads have to balance stringent performance constraints within limited power budgets. High performance computer processing units (CPUs) and graphics processing units (GPUs) cannot be used in an embedded platform due to power issues. In this letter, we propose a low power heterogeneous system consisting of an Atom processor supported by multiple accelerators that target these workloads, and seek to find if such a system can satisfy performance requirements in an energy-efficient manner. We build our low-power system using an Atom processor, an ION, a GPU, and a field-programmable gate array (FPGA)-based custom accelerator, and study its performance and power characteristics using four representative workloads. With such a system, we show an energy improvement of 42-85% over a server comprising a 2.27 GHz quadcore Xeon coupled to a 1.3 GHz 240 core Tesla GPU.