FPGA-based accelerator for losslessly quantized convolutional neural networks

Abstract

Convolutional Neural Networks (CNN) have been widely used for various computer vision tasks. While GPUs are the most common platform for CNN implementation, FPGAs are promising alternatives to provide better energy efficiency. Recent work demonstrates the potential of network quantization to reduce the model size and enhance computation efficiency while maintaining comparable accuracy to the full precision counterparts. Quantized CNN is especially suitable for FPGA implementation due to the presence of values with non-trivial bitwidth. In this paper, we present the design of an FPGA-based accelerator for losslessly quantized CNNs using High Level Synthesis tool. The experiment result shows that our design achieves 12.9 GOPS/Watt for quantized Alexnet on Imagnet Dataset.