DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference

Jiajun Zhou, Jiajun Wu, Yizhao Gao, Yuhao Ding, Chaofan Tao, Boyu Li, Fengbin Tu, Kwang-Ting Cheng, Hayden Kwok-Hay So, Ngai Wong

Abstract: To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into low-bitwidth numbers without significant accuracy degradation, especially at very low bitwidths $( < 8$ bits). This work targets an adaptive data representation with variable-length encoding called DyBit. DyBit can dynamically adjust the precision and range of separate bit-fields to be adapted to the DNN weights/activations distribution. We also propose a hardware-aware quantization framework with a mixed-precision accelerator to tradeoff the inference accuracy and speedup. Experimental results demonstrate that the ImageNet inference accuracy via DyBit is 1.97% higher than the state-of-the-art at 4-bit quantization, and the proposed framework can achieve up to $8.1\times $ speedup compared with the original ResNet-50 model.