[17] Kai Jia and Martin Rinard. Exploiting verified neural networks via floating point numerical error. In International Symposium on Static Analysis (SAS), pages 191-205. Springer, 2021.
[18] Sayash Kapoor and Arvind Narayanan. Leakage and the reproducibility crisis in ML-based science. arXiv, 2022. arXiv Computing Research Repository (CoRR), abs/2207.07048.
[19] Nachiket Kapre and Andre DeHon. Optimistic parallelization of floating-point accumulation. In Symposium on Computer Arithmetic (ARITH), pages 205-216. IEEE, 2007.
[20] Paul C. Kocher. Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In Advances in Cryptology (CRYPTO), pages 104-113. Springer, 1996.
[21] Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset. Master's thesis, University of Toronto, 2014.
[22] Yuhang Li, Mingzhu Shen, Jian Ma, Yan Ren, Mingxin Zhao, Qi Zhang, Ruihao Gong, Fengwei Yu, and Junjie Yan. Mqbench: Towards reproducible and deployable model quantization benchmark. In Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2021.
[23] Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, and Yida Wang. Optimizing CNN model inference on CPUs. In USENIX Annual Technical Conference (USENIX ATC), pages 1025-1040. USENIX Association, 2019.
[24] Raimund Meyer. Error analysis and comparison of FFT implementation structures. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 888-891. IEEE, 1989.
[25] Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis. PPFL: Privacy-preserving federated learning with trusted execution environments. In International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 94-108. ACM, 2021.
[26] Jean-Michel Muller.  $a \cdot (x \cdot x)$  or  $(a \cdot x) \cdot x$ ? In Symposium on Computer Arithmetic (ARITH), pages 17-24. IEEE, 2021.
[27] NeurIPS. NeurIPS 2021 paper checklist. https://neurips.cc/Conferences/2021/PaperInformation/PaperChecklist, 2021.
[28] NVidia Corporation. NVidia TensorCores. https://www.nvidia.com/en-us/data-center/tensor-cores/, 2022.
[29] NVidia Corporation. Optimizing convolutional layers in deep learning. https://docs.nvidia.com/deeplearning/performance/pdf/ Optimizing-Convolutional-Layers-User-Guide.pdf, 2023.
[30] PapersWithCode. Papers with code. https://paperswithcode.com/, 2018.
[31] Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, and Nachiappan Nagappan. Problems and opportunities in training deep learning software systems: An analysis of variance. In IEEE/ACM International Conference on Automated Software Engineering, pages 771-783. IEEE, 2020.
[32] Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alché Buc, Emily Fox, and Hugo Larochelle. Improving reproducibility in machine learning research (a report from the NeurIPS 2019 reproducibility program). Journal of Machine Learning Research, 22(1):7459-7478, 2021.
[33] Edward Raff. A step toward quantifying independently reproducible machine learning research. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[34] Alexander Schlögl, Tobias Kupek, and Rainer Böhme. Forensicability of deep neural network inference pipelines. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 2515–2519. IEEE, 2021.