Hawkeye: Reproducing GPU-Level Non-Determinism

Computer Science > Cryptography and Security [Submitted on 20 Mar 2026] Hawkeye: Reproducing GPU-Level Non-Determinism Erez Badash, Dan Boneh, Ilan Komargodski, Megha Srivastava We present Hawkeye, a system for analyzing and reproducing GPU-level arithmetic operations. Using our framework, anyone can re-execute on a CPU the exact matrix multiplication operations underlying a machine learning model training or inference workflow that was executed on an NVIDIA GPU, without any precision loss. This is in stark contrast to prior approaches to verifiable machine learning, which either introduce significant computation overhead to the original model owner, or suffer from non-robustness and quality degradation. The main technical contribution of Hawkeye is a systematic sequence of carefully crafted tests that study rounding direction, subnormal number handling, and order of (non-associative) accumulation during matrix multiplication on NVIDIA's Tensor Cores. We test and evaluate our framework on multiple NVIDIA GPU architectures ( Ampere, Hopper, and Lovelace) and precision types (FP16, BFP16, FP8). In all test cases, Hawkeye enables perfect reproduction of matrix multiplication on a CPU, paving the way for efficient and trustworthy third-party auditing of ML model training and inference. Comments: Accepted to MLSys 2026 Subjects: Cryptography and Security (cs.CR); Hardware Architecture (cs.AR); Machine Learning (cs.LG); Numerical Analysis (math.NA) Cite as: arXiv:2603.20421 [cs.CR]   (or arXiv:2603.20421v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.20421 Focus to learn more Submission history From: Megha Srivastava [view email] [v1] Fri, 20 Mar 2026 18:41:24 UTC (44 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AR cs.LG cs.NA math math.NA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)