HEAM: High-Efficiency Approximate Multiplier optimization for Deep Neural Networks
HEAM: High-Efficiency Approximate Multiplier optimization for Deep Neural Networks
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. …