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.

翻译：本文提出了一种利用减法器来提升CNN推理加速器性能的新方法。所提出的CNN预处理加速器通过对权重进行排序、分组和舍入，组合出可在推理期间执行卷积时，将一次乘法运算和一次加法运算替换为单次减法运算的权重组合。鉴于乘法在功耗和面积方面的高昂代价，将其替换为减法可通过降低功耗和面积来提升性能。该方法允许通过增加或减少减法器的使用来控制性能提升与精度损失之间的权衡。在舍入大小为0.05且采用LeNet-5结合MNIST数据集的情况下，所提出的设计能够实现32.03%的功耗节省和24.59%的面积缩减，而精度损失仅为0.1%。