{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:16:39Z","timestamp":1742962599889,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030667696"},{"type":"electronic","value":"9783030667702"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-66770-2_15","type":"book-chapter","created":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T10:59:13Z","timestamp":1610189953000},"page":"201-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Inference with Artificial Neural Networks on Analog Neuromorphic Hardware"],"prefix":"10.1007","author":[{"given":"Johannes","family":"Weis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Spilger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Billaudelle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yannik","family":"Stradmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arne","family":"Emmel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Breitwieser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Gr\u00fcbl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joscha","family":"Ilmberger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vitali","family":"Karasenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitja","family":"Kleider","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Mauch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Korbinian","family":"Schreiber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Schemmel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,10]]},"reference":[{"key":"15_CR1","unstructured":"Brown, T.B., et al.: Language models are few-shot learners (2020). arXiv: 2005.14165 [cs.CL]"},{"key":"15_CR2","unstructured":"Schwartz, R., Dodge, J., Smith, N.A., Etzioni, O.: Green AI. (2019). arXiv: 1907.10597 [cs.CY]"},{"issue":"6","key":"15_CR3","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1016\/j.patcog.2004.01.013","volume":"37","author":"K-S Oh","year":"2004","unstructured":"Oh, K.-S., Jung, K.: GPU implementation of neural networks. Pattern Recogn. 37(6), 1311\u20131314 (2004)","journal-title":"Pattern Recogn."},{"key":"15_CR4","unstructured":"Micikevicius, P., et al.: Mixed precision training. arXiv preprint arXiv:1710.03740 (2017)"},{"key":"15_CR5","unstructured":"Jouppi, N.P., et al.: In-datacenter performance analysis of a tensor processing unit. In: Proceedings of the 44th Annual International Symposium on Computer Architecture, pp. 1\u201312 (2017)"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Yin, S., et al.: A 1.06-to-5.09 TOPS\/W reconfigurable hybrid-neural-network processor for deep learning applications. In: 2017 Symposium on VLSI Circuits, pp. C26\u2013C27 (2017)","DOI":"10.23919\/VLSIC.2017.8008534"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1109\/4.104196","volume":"26","author":"BE Boser","year":"1991","unstructured":"Boser, B.E.: An analog neural network processor with programmable topology. IEEE J. Solid-State Circ. 26, 2017\u20132025 (1991)","journal-title":"IEEE J. Solid-State Circ."},{"key":"15_CR8","unstructured":"Yamaguchi, M., Iwamoto, G., Tamukoh, H., Morie, T.: An energy-efficient time-domain analog VLSI neural network processor based on a pulse-width modulation approach. arXiv preprint (2019). arXiv: 1902.07707 [cs.ET]"},{"key":"15_CR9","unstructured":"Schemmel, J., Billaudelle, S., Dauer, P., Weis, J.: Accelerated analog neuromorphic computing. arXiv preprint (2020). arXiv: 2003.11996 [cs.NE]"},{"key":"15_CR10","unstructured":"LeCun, Y., Cortes, C.: The MNIST database of handwritten digits (1998)"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Schmitt, S., et al.: Classification With deep neural networks on an accelerated analog neuromorphic system. In: Proceedings of the 2017 IEEE International Joint Conference on Neural Networks (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7966125","DOI":"10.1109\/IJCNN.2017.7966125"},{"key":"15_CR12","unstructured":"Cramer, B., et al.: Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate. arXiv preprint (2020). arXiv: 2006.07239 [cs.NE]"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1152\/jn.00686.2005","volume":"94","author":"R Brette","year":"2005","unstructured":"Brette, R., Gerstner, W.: Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637\u20133642 (2005). https:\/\/doi.org\/10.1152\/jn.00686.2005","journal-title":"J. Neurophysiol."},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Spilger, P., et al.: hxtorch: PyTorch for BrainScaleS-2 \u2014 perceptrons on analog neuromorphic hardware. In: Gama, J., et al. (eds.) ITEM 2020\/IoT Streams 2020. CCIS, vol. 1325, pp. 189\u2013200. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66770-2_14","DOI":"10.1007\/978-3-030-66770-2_14"},{"key":"15_CR15","unstructured":"M\u00fcller, E., et al.: Extending BrainScaleS OS for BrainScaleS-2. arXiv preprint (2020). arXiv: 2003.13750 [cs.NE]"},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"260","DOI":"10.3389\/fnins.2019.00260","volume":"13","author":"T Wunderlich","year":"2019","unstructured":"Wunderlich, T., et al.: Demonstrating advantages of neuromorphic computation: a pilot study. Front. Neurosci. 13, 260 (2019). https:\/\/doi.org\/10.3389\/fnins.2019.00260","journal-title":"Front. Neurosci."},{"key":"15_CR17","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates Inc. (2019)"},{"key":"15_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv: 1412.6980 [cs.LG]"},{"key":"15_CR19","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems (2015)"},{"key":"15_CR20","unstructured":"Edge TPU performance benchmarks (2020). https:\/\/coral.ai\/docs\/edgetpu\/benchmarks\/"}],"container-title":["Communications in Computer and Information Science","IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66770-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T11:56:22Z","timestamp":1619265382000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66770-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030667696","9783030667702"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66770-2_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"10 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ITEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"item-ws2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.item-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Co-located with ECML PKDD 2020. Due to the COVID-19 pandemic the workshop was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}