{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:34:07Z","timestamp":1762166047193,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819510207"},{"type":"electronic","value":"9789819510214"}],"license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-1021-4_12","type":"book-chapter","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:28:56Z","timestamp":1762165736000},"page":"160-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["STAMP: Accelerating Second-Order DNN Training Via ReRAM-Based Processing-in-Memory Architecture"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8291-6896","authenticated-orcid":false,"given":"Yilong","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8769-293X","authenticated-orcid":false,"given":"Fangxin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Mingyu","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Xiaoyao","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Qidong","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Chengyang","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Naifeng","family":"Jing","sequence":"additional","affiliation":[]},{"given":"Li","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Balasubramonian, R., et al.: CACTI 7: new tools for interconnect exploration in innovative off-chip memories. ACM Trans. Archit, Code Optim. (2017)","DOI":"10.1145\/3085572"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Cai, Y., et al.: Long live time: Improving lifetime for training-in-memory engines by structured gradient sparsification. In: DAC (2018)","DOI":"10.1145\/3195970.3196071"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Cao, W., et al.: Neural-PIM: efficient processing-in-memory with neural approximation of peripherals. IEEE Trans. Comput. (2021)","DOI":"10.1109\/TC.2021.3122905"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Chen, M., et al.: Thor, trace-based hardware-driven layer-oriented natural gradient descent computation. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i8.16867"},{"key":"12_CR5","unstructured":"Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Feinberg, B., et al.: An analog preconditioner for solving linear systems. In: HPCA (2021)","DOI":"10.1109\/HPCA51647.2021.00069"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. arXiv (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., et al.: Reducing the dimensionality of data with neural networks. Science (2006)","DOI":"10.1126\/science.1127647"},{"key":"12_CR10","unstructured":"Martens, J., et al.: Optimizing neural networks with kronecker-factored approximate curvature. In: ICML (2015)"},{"key":"12_CR11","unstructured":"Mindspore: Mindspore. https:\/\/github.com\/mindspore-ai\/mindspore"},{"key":"12_CR12","unstructured":"Osawa, K., et al.: Scalable and practical natural gradient for large-scale deep learning. arXiv (2020)"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Shafiee, A., et al: ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars. In: ISCA (2016)","DOI":"10.1109\/ISCA.2016.12"},{"key":"12_CR14","unstructured":"Simonyan, K., et al.: Very deep convolutional networks for large-scale image recognition. arXiv (2015)"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Song, L., et al.: PipeLayer: a pipelined ReRAM-based accelerator for deep learning. In: HPCA (2017)","DOI":"10.1109\/HPCA.2017.55"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Sun, Z., et al.: Time complexity of in-memory solution of linear systems. IEEE Trans. Electron Devices (2020)","DOI":"10.1109\/TED.2020.2992435"},{"key":"12_CR17","unstructured":"Vogt, H., et al.: NGSpice user\u2019s manual version 34 (2021)"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, S., et al.: Aging-aware lifetime enhancement for memristor-based neuromorphic computing. In: DATE (2019)","DOI":"10.23919\/DATE.2019.8714954"}],"container-title":["Lecture Notes in Computer Science","Advanced Parallel Processing Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-1021-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:29:01Z","timestamp":1762165741000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-1021-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,4]]},"ISBN":["9789819510207","9789819510214"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-1021-4_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,4]]},"assertion":[{"value":"4 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APPT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Advanced Parallel Processing Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"appt2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.appt-conference.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}