  Deep convolution neural network has achieved great success in many artificial
intelligence applications. However, its enormous model size and massive
computation cost have become the main obstacle for deployment of such powerful
algorithm in the low power and resource-limited embedded systems. As the
countermeasure to this problem, in this work, we propose statistical weight
scaling and residual expansion methods to reduce the bit-width of the whole
network weight parameters to ternary values (i.e. -1, 0, +1), with the
objectives to greatly reduce model size, computation cost and accuracy
degradation caused by the model compression. With about 16x model compression
rate, our ternarized ResNet-32/44/56 could outperform full-precision
counterparts by 0.12%, 0.24% and 0.18% on CIFAR- 10 dataset. We also test our
ternarization method with AlexNet and ResNet-18 on ImageNet dataset, which both
achieve the best top-1 accuracy compared to recent similar works, with the same
16x compression rate. If further incorporating our residual expansion method,
compared to the full-precision counterpart, our ternarized ResNet-18 even
improves the top-5 accuracy by 0.61% and merely degrades the top-1 accuracy
only by 0.42% for the ImageNet dataset, with 8x model compression rate. It
outperforms the recent ABC-Net by 1.03% in top-1 accuracy and 1.78% in top-5
accuracy, with around 1.25x higher compression rate and more than 6x
computation reduction due to the weight sparsity.
