Fixed Point Implementation of Tiny-Yolo-v2 using OpenCL on FPGA

• Published in: International journal of advanced computer science & applications 2018, Vol.9 (10)
• Main Authors: Wai, Yap June, bin, Zulkalnain, Irwan, Sani, Kim, Lim
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Chips (memory devices); Electronic devices; Field programmable gate arrays; Image classification; Object recognition; Software development tools
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2018.091062

Deep Convolutional Neural Network (CNN) algorithm has recently gained popularity in many applications such as image classification, video analytic and object detection. Being compute-intensive and memory expensive, CNN-based algorithms are hard to be implemented on the embedded device. Although recent studies have explored the hardware implementation of CNN-based object classification models such as AlexNet and VGG, there is still a rare implementation of CNN-based object detection model on Field Programmable Gate Array (FPGA). Consequently, this study proposes the fixed-point (16-bit) implementation of CNN-based object detection model: Tiny-Yolo-v2 on Cyclone V PCIe Development Kit FPGA board using High-Level-Synthesis (HLS) tool: OpenCL. Considering FPGA resource constraints in term of computational resources, memory bandwidth, and on-chip memory, a data pre-processing approach is proposed to merge the batch normalization into convolution layer. To the best of our knowledge, this is the first implementation of Tiny-Yolo-v2 object detection algorithm on FPGA using Intel FPGA Software Development Kit (SDK) for OpenCL. Finally, the proposed implementation achieves a peak performance of 21 GOPs under 100 MHz working frequency.