Systolic Array based FPGA accelerator for Yolov3-tiny

FPGAs are increasingly significant for deploying convolutional neural network (CNN) inference models because of performance demands and power constraints in embedded and data centre applications. Object detection and classification are essential tasks in computer vision. You Only Look Once (YOLO) is a very efficient algorithm for object detection and classification with its variant Yolov3-tiny specially designed for embedded applications. This paper presents the FPGA accelerator for multiple precisions (FIXED-8, FIXED-16, FLOAT32) of YoloV3-tiny. We use a homogenous systolic array architecture with a synchronized pipeline adder tree for convolution, allowing it to be scalable for multiple variants of Yolo with a change in host driver. We evaluated the design on Terasic DE5a-Net-DDR4. The Fixed point (FP-8, FP-16) implementations attain a throughput of 57 GOPs/s (> 23%) and 46.16 GOPs/s (> 340 %). We synthesized the first FLOAT32 imnlementation attaining 11.22 GFLOPs/s.