Building Tool Chains for RISC-V AI Accelerators - Jeremy Bennett, Embecosm
Our client is developing a massively parallel 64-bit chip for AI inference workloads. To facilitate early software development, we are bringing up an AI tool flow for this chip in a QEMU RISC-V environment. In this talk, we'll share our experience of getting three key AI frameworks working with RISC-V QEMU: Pytorch, Tensorflow and the OpenXLA compiler. Our talk will share our experience addressing two key issues. We will describe the challenges we faced, their solutions and reflect on the lessons learned for future work. The first of these is simply getting the tools to effectively run in an emulated RISC-V environment. These tools are large, fast moving pieces of software with extensive external dependencies. Our second challenge is performance. AI workloads are inherently parallel, and hence run efficiently on vector enabled hardware. However RISC-V vector (RVV) is relatively new, and we experienced difficulty getting the performance we expected out of the tool flow. At the end of this talk, we hope our audience will have a better understanding of the challenges in bringing up an AI tool flow under QEMU. We hope out experience will help them bring up their own AI tool flows.
