Accurate Models of AMD Matrix Cores

Accurate Models of AMD Matrix Cores. 14845 (cs) [Submitted on 13 Sep 2026 (v1), last revised 15 Sep 2026 (this version, v2)] Title:Accurate Models of AMD Matrix Cores Authors:Faizan A Khattak, Mantas Mikaitis, Carlo J.

What happened

Graziani View a PDF of the paper titled Accurate Models of AMD Matrix Cores, by Faizan A Khattak and Mantas Mikaitis and Carlo J. 14845 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Faizan Ahmad Khattak [view email] [v1] Sun, 13 Sep 2026 23:34:01 UTC (1,297 KB) [v2] Tue, 15 Sep 2026 10:33:08 UTC (1,297 KB) Full-text links: Access Paper: View a PDF of the paper titled Accurate Models of AMD Matrix Cores, by Faizan A Khattak and Mantas Mikaitis and Carlo J.

Finally, as a proof of concept for what experimental research can be done with the models, we have utilised them in two demonstrative numerical applications, quantifying application-level accuracy differences between AMD matrix cores and the NVIDIA tensor cores. MATLAB-based software models of the matrix multipliers are then developed for each architecture and validated for bit-level reproducibility against hardware using a randomized test suite consisting of 10 million sets of random input vectors. Graziani View PDF HTML (experimental) Abstract:Matrix multipliers available on recent GPUs do not conform with the IEEE 754 floating point standard.

Features of matrix multipliers differ across vendors and architectures of the same vendor, such as accumulator width, rounding behaviour, normalisation points, intermediate underflow and overflow logic, the handling of subnormals, and the treatment of special inputs. As a result, reproducibility of small matrix multiplier results across devices is not possible and cannot be achieved by software control.

The wider picture

Implementation details of matrix multipliers are not documented, making it difficult to interpret discrepancies in the computed results. We characterise the numerical behaviour of matrix multipliers across three AMD GPU architectures: CDNA 1, CDNA 2, and CDNA 3, using the MI100, MI210/250, and MI300A/300X GPUs, respectively. To achieve this, we applied a previously developed technique to iteratively refine the accuracy of the models in a loop, by randomized testing followed by test-refinement until the model matches the hardware for every test case.

Skip to main content Press Enter to search · Advanced search Computer Science > Hardware Architecture arXiv:2609. We design test vectors to target numerical features for all supported input formats and provide the derivation and the reasoning for why each vector allows to determine a particular numerical feature based on the outputs of the devices. GrazianiView PDFHTML (experimental)TeX Source view license Current browse context: cs. AR new | recent | 2026-09 Change to browse by: cs cs. MS References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading. . .

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