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| 1 | +--- |
| 2 | +contributor: max |
| 3 | +date: '2025-02-02T12:11:00' |
| 4 | +title: 'Building Tool Chains for RISC-V AI Accelerators' |
| 5 | +external_url: 'https://www.youtube.com/watch?v=DqNWF26A8Io' |
| 6 | +type: presentation |
| 7 | +tags: |
| 8 | + - oneapi |
| 9 | + - risc-v |
| 10 | +featuring: |
| 11 | + - name: Jeremy Bennett |
| 12 | + affiliation_at_video_production_time: Embecosm |
| 13 | +--- |
| 14 | + |
| 15 | +Building Tool Chains for RISC-V AI Accelerators - Jeremy Bennett, Embecosm |
| 16 | + |
| 17 | +Our client is developing a massively parallel 64-bit chip for AI inference workloads. |
| 18 | +To facilitate early software development, we are bringing up an AI tool flow for this |
| 19 | +chip in a QEMU RISC-V environment. In this talk, we'll share our experience of getting |
| 20 | +three key AI frameworks working with RISC-V QEMU: Pytorch, Tensorflow and the OpenXLA |
| 21 | +compiler. Our talk will share our experience addressing two key issues. We will describe |
| 22 | +the challenges we faced, their solutions and reflect on the lessons learned for future work. |
| 23 | +The first of these is simply getting the tools to effectively run in an emulated |
| 24 | +RISC-V environment. These tools are large, fast moving pieces of software with extensive |
| 25 | +external dependencies. Our second challenge is performance. AI workloads are inherently |
| 26 | +parallel, and hence run efficiently on vector enabled hardware. However RISC-V vector (RVV) |
| 27 | +is relatively new, and we experienced difficulty getting the performance we expected |
| 28 | +out of the tool flow. At the end of this talk, we hope our audience will have a better |
| 29 | +understanding of the challenges in bringing up an AI tool flow under QEMU. We hope our |
| 30 | +experience will help them bring up their own AI tool flows. |
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