{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:02Z","timestamp":1750309502762,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["501300923"],"award-info":[{"award-number":["501300923"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,1,20]]},"DOI":"10.1145\/3658617.3697745","type":"proceedings-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T14:32:21Z","timestamp":1741098741000},"page":"1413-1419","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Functional Safety of Neural Network Hardware Accelerators: Concurrent Out-of-Distribution Detection in Hardware Using Power Side-Channel Analysis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9775-5861","authenticated-orcid":false,"given":"Vincent","family":"Meyers","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7583-2376","authenticated-orcid":false,"given":"Michael","family":"Hefenbrock","sequence":"additional","affiliation":[{"name":"RevoAI GmbH, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1945-155X","authenticated-orcid":false,"given":"Mahboobe","family":"Sadeghipourrudsari","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2839-4692","authenticated-orcid":false,"given":"Dennis","family":"Gnad","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8829-5610","authenticated-orcid":false,"given":"Mehdi","family":"Tahoori","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Computer Vision. IEEE.","author":"Chan Robin","year":"2021","unstructured":"Robin Chan, Matthias Rottmann, and Hanno Gottschalk. 2021. Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In International Conference on Computer Vision. IEEE."},{"key":"e_1_3_2_1_2_1","volume-title":"Learning confidence for out-of-distribution detection in neural networks. arXiv preprint arXiv:1802.04865","author":"DeVries Terrance","year":"2018","unstructured":"Terrance DeVries and Graham W Taylor. 2018. Learning confidence for out-of-distribution detection in neural networks. arXiv preprint arXiv:1802.04865 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. PMLR.","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning. PMLR."},{"key":"e_1_3_2_1_4_1","volume-title":"Conference on Computer Vision and Pattern Recognition. IEEE.","author":"Hein Matthias","year":"2019","unstructured":"Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf. 2019. Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In Conference on Computer Vision and Pattern Recognition. IEEE."},{"key":"e_1_3_2_1_5_1","volume-title":"Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joe Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. 2019. Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132 (2019)."},{"key":"e_1_3_2_1_6_1","volume-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)."},{"key":"e_1_3_2_1_7_1","volume-title":"Detection of Traffic Signs in Real-World Images: The German Traffic Sign Detection Benchmark. In International Joint Conference on Neural Networks.","author":"Houben Sebastian","year":"2013","unstructured":"Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel. 2013. Detection of Traffic Signs in Real-World Images: The German Traffic Sign Detection Benchmark. In International Joint Conference on Neural Networks."},{"key":"e_1_3_2_1_8_1","volume-title":"PyTorch-OOD: A Library for Out-of-Distribution Detection Based on PyTorch. In International Conference on Computer Vision and Pattern Recognition. IEEE.","author":"Kirchheim Konstantin","year":"2022","unstructured":"Konstantin Kirchheim, Marco Filax, and Frank Ortmeier. 2022. PyTorch-OOD: A Library for Out-of-Distribution Detection Based on PyTorch. In International Conference on Computer Vision and Pattern Recognition. IEEE."},{"volume-title":"Differential power analysis","author":"Kocher Paul","key":"e_1_3_2_1_9_1","unstructured":"Paul Kocher, Joshua Jaffe, and Benjamin Jun. 1999. Differential power analysis. In CRYPTO. Springer."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/technologies8030046"},{"key":"e_1_3_2_1_11_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_13_1","volume-title":"Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690","author":"Liang Shiyu","year":"2017","unstructured":"Shiyu Liang, Yixuan Li, and Rayadurgam Srikant. 2017. Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"Energy-based out-of-distribution detection. Advances in Neural Information Processing Systems","author":"Liu Weitang","year":"2020","unstructured":"Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. 2020. Energy-based out-of-distribution detection. Advances in Neural Information Processing Systems (2020)."},{"key":"e_1_3_2_1_15_1","unstructured":"Microsoft. 2022. Deploy ML models to field-programmable gate arrays (FP-GAs) with Azure Machine Learning. https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/v1\/how-to-deploy-fpga-web-service"},{"key":"e_1_3_2_1_16_1","unstructured":"Mohammed Ali mnmoustafa. 2017. Tiny ImageNet. https:\/\/kaggle.com\/competitions\/tiny-imagenet"},{"key":"e_1_3_2_1_17_1","volume-title":"Mnist-c: A robustness benchmark for computer vision. arXiv preprint arXiv:1906.02337","author":"Mu Norman","year":"2019","unstructured":"Norman Mu and Justin Gilmer. 2019. Mnist-c: A robustness benchmark for computer vision. arXiv preprint arXiv:1906.02337 (2019)."},{"key":"e_1_3_2_1_18_1","volume-title":"NIPS workshop on deep learning and unsupervised feature learning","volume":"2011","author":"Netzer Yuval","year":"2011","unstructured":"Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. 2011. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, Vol. 2011. Granada, 4."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","unstructured":"Alessandro Pappalardo. 2023. Xilinx\/brevitas. 10.5281\/zenodo.3333552","DOI":"10.5281\/zenodo.3333552"},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Wireless and Optical Communications Networks (WOCN). IEEE.","author":"Shende Roshni","year":"2016","unstructured":"Roshni Shende and Dayanand D Ambawade. 2016. A side channel based power analysis technique for hardware trojan detection using statistical learning approach. In International Conference on Wireless and Optical Communications Networks (WOCN). IEEE."},{"key":"e_1_3_2_1_21_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_22_1","volume-title":"RDS: FPGA Routing Delay Sensors for Effective Remote Power Analysis Attacks. IACR Transactions on Cryptographic Hardware and Embedded Systems","author":"Spielmann David","year":"2023","unstructured":"David Spielmann, Ognjen Glamo\u010danin, and Mirjana Stojilovi\u0107. 2023. RDS: FPGA Routing Delay Sensors for Effective Remote Power Analysis Attacks. IACR Transactions on Cryptographic Hardware and Embedded Systems (2023)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021744"},{"key":"e_1_3_2_1_24_1","volume-title":"Carlos Aguayo Gonzalez, and Peter Chin","author":"Wang Xiao","year":"2018","unstructured":"Xiao Wang, Quan Zhou, Jacob Harer, Gavin Brown, Shangran Qiu, Zhi Dou, John Wang, Alan Hinton, Carlos Aguayo Gonzalez, and Peter Chin. 2018. Deep learning-based classification and anomaly detection of side-channel signals. In Cyber Sensing. SPIE."},{"key":"e_1_3_2_1_25_1","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:cs.LG\/1708.07747 [cs.LG]"},{"key":"e_1_3_2_1_26_1","volume-title":"Openood: Benchmarking generalized out-of-distribution detection. Advances in Neural Information Processing Systems","author":"Yang Jingkang","year":"2022","unstructured":"Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, et al. 2022. Openood: Benchmarking generalized out-of-distribution detection. Advances in Neural Information Processing Systems (2022)."},{"key":"e_1_3_2_1_27_1","volume-title":"Cnnlab: a novel parallel framework for neural networks using gpu and fpga-a practical study with tradeoff analysis. arXiv preprint arXiv:1606.06234","author":"Zhu Maohua","year":"2016","unstructured":"Maohua Zhu, Liu Liu, Chao Wang, and Yuan Xie. 2016. Cnnlab: a novel parallel framework for neural networks using gpu and fpga-a practical study with tradeoff analysis. arXiv preprint arXiv:1606.06234 (2016)."}],"event":{"name":"ASPDAC '25: 30th Asia and South Pacific Design Automation Conference","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEICE","IPSJ","IEEE CAS","IEEE CEDA"],"location":"Tokyo Japan","acronym":"ASPDAC '25"},"container-title":["Proceedings of the 30th Asia and South Pacific Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3658617.3697745","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3658617.3697745","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:50Z","timestamp":1750295870000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3658617.3697745"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,20]]},"references-count":27,"alternative-id":["10.1145\/3658617.3697745","10.1145\/3658617"],"URL":"https:\/\/doi.org\/10.1145\/3658617.3697745","relation":{},"subject":[],"published":{"date-parts":[[2025,1,20]]},"assertion":[{"value":"2025-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}