Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914307007053824 |
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| author | Cavagna, Hiari Pizzini Cesarini, Daniele Bartolini, Andrea |
| author_facet | Cavagna, Hiari Pizzini Cesarini, Daniele Bartolini, Andrea |
| contents | The increasing demand for generative AI as Large Language Models (LLMs) services has driven the need for specialized hardware architectures that optimize computational efficiency and energy consumption. This paper evaluates the performance of the Tenstorrent Grayskull e75 RISC-V accelerator for basic linear algebra kernels at reduced numerical precision, a fundamental operation in LLM computations. We present a detailed characterization of Grayskull's execution model, gridsize, matrix dimensions, data formats, and numerical precision impact computational efficiency. Furthermore, we compare Grayskull's performance against state-of-the-art architectures with tensor acceleration, including Intel Sapphire Rapids processors and two NVIDIA GPUs (V100 and A100). Whilst NVIDIA GPUs dominate raw performance, Grayskull demonstrates a competitive trade-off between power consumption and computational throughput, reaching a peak of 1.55 TFLOPs/Watt with BF16. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_06085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities Cavagna, Hiari Pizzini Cesarini, Daniele Bartolini, Andrea Performance Artificial Intelligence Hardware Architecture The increasing demand for generative AI as Large Language Models (LLMs) services has driven the need for specialized hardware architectures that optimize computational efficiency and energy consumption. This paper evaluates the performance of the Tenstorrent Grayskull e75 RISC-V accelerator for basic linear algebra kernels at reduced numerical precision, a fundamental operation in LLM computations. We present a detailed characterization of Grayskull's execution model, gridsize, matrix dimensions, data formats, and numerical precision impact computational efficiency. Furthermore, we compare Grayskull's performance against state-of-the-art architectures with tensor acceleration, including Intel Sapphire Rapids processors and two NVIDIA GPUs (V100 and A100). Whilst NVIDIA GPUs dominate raw performance, Grayskull demonstrates a competitive trade-off between power consumption and computational throughput, reaching a peak of 1.55 TFLOPs/Watt with BF16. |
| title | Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities |
| topic | Performance Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2505.06085 |