Bit-width Adaptive Accelerator Design for Convolution Neural Network

Abstract

Convolutional neural networks (CNNs) have achieved great success in many applications. Recently, various FPGA-based accelerators have been proposed to improve the performance of CNNs. However, current most FPGA-based methods only use the same bit-width selection for all CNN layers which lead to very low resource utilization and difficulty in further performance improvement. In this paper, we propose a bit-width adaptive accelerator design approach which can adapt to the CNN layers with various bit-width requirements in a same network. We construct multiple different bit-width convolutional processors to compute the CNN layers in parallel way. We partition the FPGA DSP resources and use our optimization approach to find the optimal resource allocation. On a Xilinx Virtex-7 FPGA, our design approach achieves higher throughput than the state-of-the-art FPGA-based CNN accelerators from 5.48× to 7.25× and by 6.20× on average, when we evaluate the convolutional layers of AlexNet and deeper VGG CNNs.