HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
This work addresses efficiency improvements in DNN accelerators for hardware designers, though it is incremental as it builds on existing approximate multiplier methods.
The paper tackles the problem of designing approximate multipliers for deep neural networks by optimizing them based on operand distributions, achieving up to 50.24% higher accuracy than existing approximate multipliers with reductions in area, power, and delay.
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.