An energy-efficient convolutional neural network accelerator for speech classification based on FPGA and quantization

Deep convolutional neural network (CNN), which is widely applied in image tasks, can also achieve excellent performance in acoustic tasks. However, activation data in convolutional neural network is usually indicated in floating format, which is both time-consuming and power-consuming when be computed. Quantization method can turn activation data into fixed-point, replacing floating computing into faster and more energy-saving fixed-point computing. Based on this method, this article proposes a design space searching method to quantize a binary weight neural network. A specific accelerator is built on FPGA platform, which has layer-by-layer pipeline design, higher throughput and energy-efficiency compared with CPU and other hardware platforms.