Sensor-Based Approximate Adder Design for Accelerating Error-Tolerant and Deep-Learning Applications

Abstract

Approximate computing is an emerging strategy which trades computational accuracy for computational cost in terms of performance, energy, and/or area. In this paper, we propose a novel sensor-based approximate adder for high-performance energy-efficient arithmetic computation, while considering the accuracy requirement of error-tolerant applications. This is the first work using in-situ sensors for approximate adder design, based on monitoring online transition activity on the carry chain and speculating on carry propagation/truncation. On top of a fully-optimized ripple-carry adder, the performance of our adder is enhanced by 2.17X. When applied in error-tolerant applications such as image processing and handwritten digit recognition, our approximate adder leads to very promising quality of results compared to the case when an accurate adder is used.