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As RISC-V matures into a viable architecture for AI and data center workloads, bringing first-class PyTorch support to the ecosystem is a critical milestone. This session provides a technical deep dive into the ongoing efforts to port PyTorch natively to RISC-V, moving beyond experimental cross-compilation toward a stable, tested, and optimized environment. We detail the challenges of reconciling native math library dependencies like OpenBLAS and oneDNN with RISC-V Vector (RVV) extensions, alongside the work required to upstream these accelerations to ensure sustainable, long-term performance.
The talk also addresses the critical "last mile" of the Python ecosystem: ensuring that the broader dependency tree—including NumPy, SciPy, and ONNX—is natively available and performant on the architecture. Finally, we examine the primary bottleneck for official support: CI infrastructure. We outline the roadmap for transitioning from tagged cross-compilation to a native testing pool, discussing the logistics of maintaining a reliable hardware fleet to meet the high-volume validation standards required for the PyTorch master branch and pull request workflows.
Ludovic works at the intersection of open-source software and emerging hardware. He is a key contributor to the RISC-V ecosystem, focusing on the performance and stability of the AI stack. His recent work involves optimizing native dependencies like OpenBLAS and oneDNN and establishing... Read More →
