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

Autor:	Zheng, Su, Li, Zhen, Lu, Yao, Gao, Jingbo, Zhang, Jide, Wang, Lingli
Rok vydání:	2022
Předmět:	Computer Science - Hardware Architecture Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 2022 IEEE International Symposium on Circuits and Systems (ISCAS)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.08022 Zobrazit plný text záznamu View this record from Arxiv