Accelerating Tiny YOLOv3 using FPGA-based Hardware/Software Co-Design

Convolutional Neural Networks (CNNs) are influencing major breakthroughs in computer vision by achieving unprecedented accuracy on tasks such as image classification, object detection, landmark detection and semantic segmentation. Owing to high computational complexity of most modern CNN architectures, graphical processing units (GPUs) are being utilized to achieve real-time performance albeit at a high energy cost. Consequently, Field Programmable Gate Arrays (FPGAs) based hardware accelerators are also making their way as they demonstrate GPU-like performance with significantly lower energy consumption that is well-suited for embedded vision applications. In this paper, we employ Hardware/Software Co-Design approach to accelerate Tiny YOLOv3 - an efficient CNN architecture for object detection - by designing a hardware accelerator for convolution, the most complex operation involved in the CNNs. Experimental results show significant performance gains, in the range of 3.9x to 21.3x, over previous implementations of efficient object detection algorithms.