The input data is converted to a high dimensional bit-sliced format. In the post-training stage, we analyze the impact of different bit slices to the accuracy. By pruning the redundant input bit slices and shrinking the network size, we are able to build a more compact BNN. Our result ...
The former reduces network size from the whole by searching optimal cell structure, while the latter compresses the network locally by removing unimportant connections in networks with weight-ranking-based pruning. These two methods will separately and sequentially lighten CNN from different scales, ...
(1) ‖⋅‖Fis Frobenius norm.XXcisN×kh×kwmatrix sliced fromc-th channel of input volumesXX,c=1,2,…,ni.WWcisni×kh×kwfilter weights sliced fromc-th channel ofWW.ββis coefficient vector of lengthnifor channel selection, andβc(c-th entry ofββ) is a scalar mask toc-th chan...