  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.
