RISC-V Based MPSoC Design Exploration for FPGAs: Area, Power and Performance

Abstract

Modern image processing applications, like object detection or image segmentation, require high computation and have high memory requirements. For ASIC-/FPGA-based architectures, hardware accelerators are a promising solution, but they lack flexibility and programmability. To fulfill flexibility, computational and memory intensive characteristics of these applications in embedded systems, we propose a modular and flexible RISC-V based MPSoC architecture on Xilinx Zynq Ultrascale+ MPSoC. The proposed architecture can be ported to other Xilinx FPGAs. Two neural networks (Lenet-5 and Cifar-10 example) were used as test applications to evaluate the proposed MPSoC architectures. To increase the performance and efficiency, different optimization techniques were adapted on the MPSoC and results were evaluated. 16-bit fixed-point parameters were used to have a compression of 50% in data size and algorithms were parallelized and mapped on the proposed MPSoC to achieve higher performance. A 4x parallelization of a NN algorithm on the proposed MPSoC resulted in 3.96x speed up and consumed 3.61x less energy as compared to a single soft-core processor setup.