This paper presents a novel method to boost the performance of CNN inference
accelerators by utilizing subtractors. The proposed CNN preprocessing
accelerator relies on sorting, grouping, and rounding the weights to create
combinations that allow for the replacement of one multiplication operation and
addition operation by a single subtraction operation when applying convolution
during inference. Given the high cost of multiplication in terms of power and
area, replacing it with subtraction allows for a performance boost by reducing
power and area. The proposed method allows for controlling the trade-off
between performance gains and accuracy loss through increasing or decreasing
the usage of subtractors. With a rounding size of 0.05 and by utilizing LeNet-5
with the MNIST dataset, the proposed design can achieve 32.03% power savings
and a 24.59% reduction in area at the cost of only 0.1% in terms of accuracy
loss.

该研究提出了一种利用减法器提高 CNN 推理加速器性能的新方法，通过排序、分组和舍入权重来创建组合，从而可以在推理期间将一个乘法和加法运算替换为一个减法运算，从而降低功耗和面积，并通过增加或减少减法器的使用来控制性能增益和准确性损失的权衡。利用 MNIST 数据集和 LeNet-5，采用舍入大小为 0.05 的设计可以实现 32.03％的功耗节省和 24.59％的面积减少，仅以 0.1％的准确性损失为代价。