  1 bit deep neural networks (DNNs), of which both the activations and weights
are binarized , are attracting more and more attention due to their high
computational efficiency and low memory requirement . However, the drawback of
large accuracy dropping also restrict s its application. In this paper, we
propose a novel Targeted Acceleration and Compression (TAC) framework to
improve the performance of 1 bit deep neural networks W e consider that the
acceleration and compression effects of binarizing fully connected layer s are
not sufficient to compensate for the accuracy loss caused by it In the proposed
framework, t he convolutional and fully connected layer are separated and
optimized i ndividually . F or the convolutional layer s , both the activations
and weights are binarized. For the fully connected layer s, the binarization
operation is re placed by network pruning and low bit quantization. The
proposed framework is implemented on the CIFAR 10, CIFAR 100 and ImageNet (
ILSVRC 12 ) datasets , and experimental results show that the proposed TAC can
significantly improve the accuracy of 1 bit deep neural networks and
outperforms the state of the art by more than 6 percentage points .
