Deep neural networks (DNNs) have been successfully applied in various fields.
A major challenge of deploying DNNs, especially on edge devices, is power
consumption, due to the large number of multiply-and-accumulate (MAC)
operations. To address this challenge, we propose PowerPruning, a novel method
to reduce power consumption in digital neural network accelerators by selecting
weights that lead to less power consumption in MAC operations. In addition, the
timing characteristics of the selected weights together with all activation
transitions are evaluated. The weights and activations that lead to small
delays are further selected. Consequently, the maximum delay of the sensitized
circuit paths in the MAC units is reduced even without modifying MAC units,
which thus allows a flexible scaling of supply voltage to reduce power
consumption further. Together with retraining, the proposed method can reduce
power consumption of DNNs on hardware by up to 78.3% with only a slight
accuracy loss.

本文提出 PowerPruning 方法，通过选择在 MAC 操作中消耗更少功率的权重，以及考虑所选择权重的时序特征和所有激活转移，进一步选择导致小延迟的权重和激活，从而在不修改 MAC 单位的情况下减小了 MAC 单位敏化电路路径的最大延迟，实现了进一步的供应电压灵活缩放，使得该方法能够在仅有轻微精度损失的情况下，最多可将硬件上深度神经网络的功耗降低 78.3%。