Binarized Neural Network (BNN) removes bitwidth redundancy in classical CNN by using a
single bit (-1/+ 1) for network parameters and intermediate representations, which has
greatly reduced the off-chip data transfer and storage overhead. However, a large amount of
computation redundancy still exists in BNN inference. By analyzing local properties of
images and the learned BNN kernel weights, we observe an average of $\sim $78% input
similarity and $\sim $59% weight similarity among weight kernels, measured by our …