Realization of OpenCL based CNN Implementation on FPGA using SDAccel Platform

In this paper, we are demonstrating the implementation flow of algorithms using deep Convolutional Neural Networks (CNN). Deploying CNNs solely on CPU-GPU based platforms deal with issues of high power consumption and comparatively slower processing speeds than hardware accelerators like FPGA. FPGA offers high parallel-processing capability, hardware re-configurability, reduced latency and low power consumption. To further improve flexibility in programming the hardware accelerator, we decided to use High Level Synthesis (HLS) tool based on OpenCL framework for development of our CNN model and implementing it on cloud. We are using Xilinx SDAccel development environment targeting Amazon’s AWS EC2 F1 instances. We are using Cloud based services for our model visualization on FPGAs. Cloud provides FPGA as a service with High Performance Computing (HPC) resources and enables us to deploy our optimized code on provided FPGA accelerated instance without need of buying an actual hardware. AWS EC2 F1 instance uses Xilinx Ultrascale+VU9P chip as a Hardware platform.