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

01/20/2022

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by   Su Zheng, et al.

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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 best reproduced approximate multiplier in DNNs, with 15.76 25.05 multiplier, our multiplier reduces the area, power consumption, and delay by 44.94 tested DNN accelerator modules with our multiplier obtain up to 18.70 area and 9.99