HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks

TL;DR

This paper introduces HEAM, an optimized approximate multiplier design for deep neural networks that significantly improves accuracy and efficiency, reducing area, power, and delay with minimal accuracy loss.

Contribution

It presents a novel optimization method for automatic approximate multiplier design tailored for DNNs, achieving superior accuracy and efficiency over existing solutions.

Findings

Up to 50.24% higher accuracy than previous approximate multipliers.

15.76% smaller area and 25.05% less power consumption compared to exact multipliers.

DNN modules with HEAM multipliers are up to 18.70% smaller and consume 9.99% less power.

Abstract

We propose an optimization method for the automatic design of approximate multipliers, which minimizes the average error according to the operand distributions. Our multiplier achieves up to 50.24% higher accuracy than the best reproduced approximate multiplier in DNNs, with 15.76% smaller area, 25.05% less power consumption, and 3.50% shorter delay. Compared with an exact multiplier, our multiplier reduces the area, power consumption, and delay by 44.94%, 47.63%, and 16.78%, respectively, with negligible accuracy losses. The tested DNN accelerator modules with our multiplier obtain up to 18.70% smaller area and 9.99% less power consumption than the original modules.