Quantization Through Search: A Novel Scheme to Quantize Convolutional Neural Networks in Finite Weight Space

Quantization has become an essential technique in compressing deep neural networks for deployment onto resource-constrained hardware. It is noticed that, the hardware efficiency of implementing quantized networks is highly coupled with the actual values to be quantized into, and therefore, with given bit widths, we can smartly choose a value space to further boost the hardware effi-ciency. For example, using weights of only integer powers of two, multiplication can be fulfilled by bit operations. Under such circum-stances, however, existing quantization -aware training methods are either not suitable to apply or unable to unleash the expressiveness of very low bit-widths. For the best hardware efficiency, we revisit the quantization of convolutional neural networks and propose to address the training process from a weight-searching angle, as opposed to optimizing the quantizer functions as in existing works. Extensive experiments on CIFAR10 and ImageNet classification tasks are examined with implementations onto well-established CNN architectures, such as ResNet, VGG, and MobileNet, etc. It is shown the proposed method can achieve a lower accuracy loss than the state of arts, and/or improving implementation efficiency by using hardware-friendly weight values at the same time.