A FPGA-Oriented Quantization Scheme for MobileNet-SSD

Abstract

The rising popularity of mobile devices, which have high performance in object detection calls for a method to implement our algorithms efficiently on mobile devices. As we know, Deep Learning is a good approach to achieve state-of-the-art results. But it needs lots of computation and resources, mobile devices are often resource-limited because of their small size. Recently, FPGA is a device famous for parallelism and many people try to implement the Deep Learning Networks on FPGA. After our investigation, we choose MobileNet-SSD to implement on FPGA because that this network is designed for mobile devices and its size and cost are relatively smaller. There are also some challenges about implementing the network on FPGA, such as the large demand of resources and low latency, which are pretty important for mobile devices. In this paper, we show a quantization scheme for object detection networks based on FPGA and a process to simulate the FPGA on PC to help us predict the performance of networks on FPGA. Besides, we propose an integer-only inference based on FPGA, which truly reduce the cost of resources greatly. The method of Dynamic Fixed Point is adopted and we make some improvement based on object detection networks to quantize the MobileNet-SSD, which is a suitable object detection network for embedded system. Our improvements make its performance better than Ristretto.