← 研踪 PaperTrailarXiv:2611.04740 · v1
A RISC-V Simulator for the Tenstorrent Tensix Architecture
首次提交 2026-11-03 19:57
版本更新 2026-11-03 19:57
雷达观测 2026-11-04 23:39
摘要
RISC-V based AI accelerators have become popular and not only for AI and ML, but also as vehicles for exploring HPC workloads more generally. Tenstorrent are one example, and whilst they document their Tensix architecture in detail and develop their software stack openly, running code has historically required a physical card, and their recently released simulator targets hardware equivalence rather than introspection. In this paper we present Wolfpine, a community developed RISC-V simulator for the Tensix architecture, supporting both the Wormhole and Blackhole generations and integrating seamlessly with the TT-Metal software stack. Constructed entirely from publicly available documentation, open source software and measurements on physical hardware, Wolfpine is written in pure Python for readability and ease of modification. An opt-in, provenance-tracked cost model adds timing fidelity incrementally, and typed event streams, per-component tracing and timeline export enable programmers to identify and diagnose performance bottlenecks that the hardware itself hides.
作者备注：Author accepted version of paper in the RISC-V for HPC SC26 workshop
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v1 · 2026-11-03 19:57 · 最新观测版本
A RISC-V Simulator for the Tenstorrent Tensix Architecture
RISC-V based AI accelerators have become popular and not only for AI and ML, but also as vehicles for exploring HPC workloads more generally. Tenstorrent are one example, and whilst they document their Tensix architecture in detail and develop their software stack openly, running code has historically required a physical card, and their recently released simulator targets hardware equivalence rather than introspection. In this paper we present Wolfpine, a community developed RISC-V simulator for the Tensix architecture, supporting both the Wormhole and Blackhole generations and integrating seamlessly with the TT-Metal software stack. Constructed entirely from publicly available documentation, open source software and measurements on physical hardware, Wolfpine is written in pure Python for readability and ease of modification. An opt-in, provenance-tracked cost model adds timing fidelity incrementally, and typed event streams, per-component tracing and timeline export enable programmers to identify and diagnose performance bottlenecks that the hardware itself hides.
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