{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:12:35Z","timestamp":1750219955273,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":12,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,14]]},"DOI":"10.1145\/3597031.3597058","type":"proceedings-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T20:12:13Z","timestamp":1689797533000},"page":"114-118","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Noise Resilience of Reduced Precision Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0959-3944","authenticated-orcid":false,"given":"Sai","family":"Sanjeet","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9839-7754","authenticated-orcid":false,"given":"Sannidhi","family":"Boppana","sequence":"additional","affiliation":[{"name":"Saratoga High School, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3563-9096","authenticated-orcid":false,"given":"Bibhu Datta","family":"Sahoo","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6516-4175","authenticated-orcid":false,"given":"Masahiro","family":"Fujita","sequence":"additional","affiliation":[{"name":"University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2018. Quantized neural networks on Pynq environment. https:\/\/github.com\/Xilinx\/QNN-MO-PYNQ. Accessed: 2023-02-14.  2018. Quantized neural networks on Pynq environment. https:\/\/github.com\/Xilinx\/QNN-MO-PYNQ. Accessed: 2023-02-14."},{"key":"e_1_3_2_1_2_1","unstructured":"2019. Xilinx Ultra96-v2 FPGA board. https:\/\/www.avnet.com\/wps\/portal\/us\/products\/avnet-boards\/avnet-board-families\/ultra96-v2. Accessed: 2023-02-14.  2019. Xilinx Ultra96-v2 FPGA board. https:\/\/www.avnet.com\/wps\/portal\/us\/products\/avnet-boards\/avnet-board-families\/ultra96-v2. Accessed: 2023-02-14."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3242897"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_3_2_1_5_1","volume-title":"On the Adversarial Robustness of Quantized Neural Networks. CoRR abs\/2105.00227","author":"Gorsline Micah","year":"2021","unstructured":"Micah Gorsline , James Smith , and Cory\u00a0 E. Merkel . 2021. On the Adversarial Robustness of Quantized Neural Networks. CoRR abs\/2105.00227 ( 2021 ). arXiv:2105.00227https:\/\/arxiv.org\/abs\/2105.00227 Micah Gorsline, James Smith, and Cory\u00a0E. Merkel. 2021. On the Adversarial Robustness of Quantized Neural Networks. CoRR abs\/2105.00227 (2021). arXiv:2105.00227https:\/\/arxiv.org\/abs\/2105.00227"},{"key":"e_1_3_2_1_6_1","volume-title":"Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser. CoRR abs\/1712.02976","author":"Liao Fangzhou","year":"2017","unstructured":"Fangzhou Liao , Ming Liang , Yinpeng Dong , Tianyu Pang , Jun Zhu , and Xiaolin Hu. 2017. Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser. CoRR abs\/1712.02976 ( 2017 ). arXiv:1712.02976http:\/\/arxiv.org\/abs\/1712.02976 Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, and Xiaolin Hu. 2017. Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser. CoRR abs\/1712.02976 (2017). arXiv:1712.02976http:\/\/arxiv.org\/abs\/1712.02976"},{"key":"e_1_3_2_1_7_1","volume-title":"Defensive Quantization: When Efficiency Meets Robustness. CoRR abs\/1904.08444","author":"Lin Ji","year":"2019","unstructured":"Ji Lin , Chuang Gan , and Song Han . 2019 . Defensive Quantization: When Efficiency Meets Robustness. CoRR abs\/1904.08444 (2019). arXiv:1904.08444http:\/\/arxiv.org\/abs\/1904.08444 Ji Lin, Chuang Gan, and Song Han. 2019. Defensive Quantization: When Efficiency Meets Robustness. CoRR abs\/1904.08444 (2019). arXiv:1904.08444http:\/\/arxiv.org\/abs\/1904.08444"},{"key":"e_1_3_2_1_8_1","volume-title":"Analyzing the Noise Robustness of Deep Neural Networks. CoRR abs\/1810.03913","author":"Liu Mengchen","year":"2018","unstructured":"Mengchen Liu , Shixia Liu , Hang Su , Kelei Cao , and Jun Zhu . 2018. Analyzing the Noise Robustness of Deep Neural Networks. CoRR abs\/1810.03913 ( 2018 ). arXiv:1810.03913http:\/\/arxiv.org\/abs\/1810.03913 Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, and Jun Zhu. 2018. Analyzing the Noise Robustness of Deep Neural Networks. CoRR abs\/1810.03913 (2018). arXiv:1810.03913http:\/\/arxiv.org\/abs\/1810.03913"},{"key":"e_1_3_2_1_9_1","volume-title":"YOLOv3: An Incremental Improvement. CoRR abs\/1804.02767","author":"Redmon Joseph","year":"2018","unstructured":"Joseph Redmon and Ali Farhadi . 2018. YOLOv3: An Incremental Improvement. CoRR abs\/1804.02767 ( 2018 ). arXiv:1804.02767http:\/\/arxiv.org\/abs\/1804.02767 Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. CoRR abs\/1804.02767 (2018). arXiv:1804.02767http:\/\/arxiv.org\/abs\/1804.02767"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8060661"},{"key":"e_1_3_2_1_11_1","volume-title":"Improving Adversarial Robustness in Weight-quantized Neural Networks. CoRR abs\/2012.14965","author":"Song Chang","year":"2020","unstructured":"Chang Song , Elias Fallon , and Hai\u00a0Helen Li. 2020. Improving Adversarial Robustness in Weight-quantized Neural Networks. CoRR abs\/2012.14965 ( 2020 ). arXiv:2012.14965https:\/\/arxiv.org\/abs\/2012.14965 Chang Song, Elias Fallon, and Hai\u00a0Helen Li. 2020. Improving Adversarial Robustness in Weight-quantized Neural Networks. CoRR abs\/2012.14965 (2020). arXiv:2012.14965https:\/\/arxiv.org\/abs\/2012.14965"},{"key":"e_1_3_2_1_12_1","volume-title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR abs\/1606.06160","author":"Zhou Shuchang","year":"2016","unstructured":"Shuchang Zhou , Zekun Ni , Xinyu Zhou , He Wen , Yuxin Wu , and Yuheng Zou . 2016. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR abs\/1606.06160 ( 2016 ). arXiv:1606.06160http:\/\/arxiv.org\/abs\/1606.06160 Shuchang Zhou, Zekun Ni, Xinyu Zhou, He Wen, Yuxin Wu, and Yuheng Zou. 2016. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR abs\/1606.06160 (2016). arXiv:1606.06160http:\/\/arxiv.org\/abs\/1606.06160"}],"event":{"name":"HEART 2023: The International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies 2023","acronym":"HEART 2023","location":"Kusatsu Japan"},"container-title":["Proceedings of the 13th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597031.3597058","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597031.3597058","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:05Z","timestamp":1750182545000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597031.3597058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":12,"alternative-id":["10.1145\/3597031.3597058","10.1145\/3597031"],"URL":"https:\/\/doi.org\/10.1145\/3597031.3597058","relation":{},"subject":[],"published":{"date-parts":[[2023,6,14]]},"assertion":[{"value":"2023-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}