Improved field programmable gatearraybased accelerator of deep neural networkusing opencl - Yap, June Wai (2022)
Being compute-intensive and memory expensive, it is hard to deploy Deep Neural Network (DNN) based models into the embedded devices. Despite recent studies that have shown the efforts to explore the Field Programmable Gate Array (FPGA) as an alternative to deploy DNN-based models such as AlexNet and VGG, there is still a lot of challenges to implement DNN-based object detection model on Field Programmable Gate Array (FPGA). Hence, in this research, the design of a scalable parameterised DNN-based object detection model: Tiny YOLOv2 targeting on FPGA: Cyclone V PCIE Development Kit using High-Level-Synthesis (HLS) tool is explored. Considering the hardware resource limitations in term of computational resources and memory bandwidth, data quantization is proposed to convert the floating point (32-bit) of Tiny YOLOv2 into fixed-point (8-bit) design. To achieve the good performance, an in-depth analysis on the computation complexity and memory footprint of the Tiny YOLOv2 is also studied to find the best quantization scheme for Tiny YOLOv2. The proposed quantization scheme improves the memory requirements to store the parameter from 60 MB to 15 MB, which is around ×4 times improvement compared to the original floating-point design. Finally, the proposed implementation achieves a peak performance density of 0.29 Giga-Operation Per Second (GOPS)/Digital Signal Processing Block (DSP) with only 0.4% loss in the accuracy, which the performance is comparable to all other previous works.