Implementing Deep learning MRI reconstruction on a RISC-V Hardware Accelerator

Motivation: Deep Learning MRI reconstruction requires access to high-performance compute hardware (GPUs) but reconstruction times can still remain slow. Goal(s): Use domain specific hardware accelerators to speed up deep learning MRI reconstruction compared to classic CPU/GPU implementations. Approach: We use an open source SoC design and simulation framework, Chipyard, and a neural network accelerator, Gemmini, to run inference on a pretrained MRI reconstruction network. The results are measured on FireSim, an FPGA simulation framework. Results: Simulated inference times of pretrained models on Gemmini are much faster compared to CPU and show similar image quality metrics compared to the inference run on a GPU. Impact: Shows the potential of developing custom compute hardware designed to accelerate deep learning MRI reconstruction.