Designing Efficient Shortcut Architecture for Improving the Accuracy of Fully Quantized Neural Networks Accelerator
Baoting Li, Longjun Liu, Yanmin Jin, Peng Gao, Hongbin Sun, Nanning Zheng · 2020
Network quantization is an effective solution to compress Deep Neural Networks (DNN) that can be accelerated with custom circuit. However, existing quantization methods suffer from significant loss in accuracy. In this paper, we propose an efficient shortcut architecture to enhance the representational capability of DNN between different convolution layers. We further implement the shortcut hardware architecture to effectively improve the accuracy of fully quantized neural networks accelerator. The experimental results show that our shortcut architecture can obviously improve network accuracy while increasing very few hardware resources ( 0.11 × and 0.17 × for LUT and FF respectively) compared with the whole accelerator.