  Convolutional neural network (CNN) has been widely used for vision-based
tasks. Due to the high computational complexity and memory storage requirement,
it is hard to directly deploy a full-precision CNN on embedded devices. The
hardware-friendly designs are needed for re-source-limited and
energy-constrained embed-ded devices. Emerging solutions are adopted for the
neural network compression, e.g., bina-ry/ternary weight network, pruned
network and quantized network. Among them, Binarized Neural Network (BNN) is
believed to be the most hardware-friendly framework due to its small network
size and low computational com-plexity. No existing work has further shrunk the
size of BNN. In this work, we explore the redun-dancy in BNN and build a
compact BNN (CBNN) based on the bit-level sensitivity analy-sis and bit-level
data pruning. The input data is converted to a high dimensional bit-sliced
for-mat. In post-training stage, we analyze the im-pact 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 shows that we
can further scale down the network size of the BNN up to 3.9x with no more than
1% accuracy drop. The actual runtime can be reduced up to 2x and 9.9x compared
with the baseline BNN and its full-precision counterpart, respectively.
