{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:50:51Z","timestamp":1772909451982,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585259","type":"print"},{"value":"9783030585266","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58526-6_29","type":"book-chapter","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T21:03:07Z","timestamp":1602018187000},"page":"488-503","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Fully Embedding Fast Convolutional Networks on Pixel Processor Arrays"],"prefix":"10.1007","author":[{"given":"Laurie","family":"Bose","sequence":"first","affiliation":[]},{"given":"Piotr","family":"Dudek","sequence":"additional","affiliation":[]},{"given":"Jianing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Stephen J.","family":"Carey","sequence":"additional","affiliation":[]},{"given":"Walterio W.","family":"Mayol-Cuevas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"29_CR1","first-page":"1","volume":"99","author":"A Aimar","year":"2018","unstructured":"Aimar, A., et al.: NullHop: a flexible convolutional neural network accelerator based on sparse representations of feature maps. IEEE Trans. Neural Netw. Learn. Syst. 99, 1\u201313 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Bose, L., Chen, J., Carey, S.J., Dudek, P., Mayol-Cuevas, W.: Visual odometry for pixel processor arrays. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4604\u20134612 (2017)","DOI":"10.1109\/ICCV.2017.493"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Bose, L., Chen, J., Carey, S.J., Dudek, P., Mayol-Cuevas, W.: A camera that CNNs: towards embedded neural networks on pixel processor arrays. arXiv preprint arXiv:1909.05647 (2019). (ICCV 2019 Accepted Submission)","DOI":"10.1109\/ICCV.2019.00142"},{"key":"29_CR4","unstructured":"Carey, S.J., Lopich, A., Barr, D.R., Wang, B., Dudek, P.: A 100,000 fps vision sensor with embedded 535GOPS\/W 256\u00a0$$\\times $$\u00a0256 SIMD processor array. In: 2013 Symposium on VLSI Circuits, pp. C182\u2013C183. IEEE (2013)"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Carey, S.J., Zar\u00e1ndy, \u00c1., Dudek, P.: Characterization of processing errors on analog fully-programmable cellular sensor-processor arrays. In: 2014 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1580\u20131583. IEEE (2014)","DOI":"10.1109\/ISCAS.2014.6865451"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Chen, J., Carey, S.J., Dudek, P.: Scamp5d vision system and development framework. In: Proceedings of the 12th International Conference on Distributed Smart Cameras, p. 23. ACM (2018)","DOI":"10.1145\/3243394.3243698"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y.H., Emer, J., Sze, V.: Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks. In: ACM SIGARCH Computer Architecture News, vol. 44, pp. 367\u2013379. IEEE Press (2016)","DOI":"10.1145\/3007787.3001177"},{"key":"29_CR8","unstructured":"Courbariaux, M., Bengio, Y., David, J.P.: Binaryconnect: training deep neural networks with binary weights during propagations. In: Advances in Neural Information Processing Systems, pp. 3123\u20133131 (2015)"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Du, Z., et al.: ShiDianNao: shifting vision processing closer to the sensor. In: ACM SIGARCH Computer Architecture News, vol. 43, pp. 92\u2013104. ACM (2015)","DOI":"10.1145\/2872887.2750389"},{"key":"29_CR10","unstructured":"Guillard, B.: Optimising convolutional neural networks for super fast inference on focal-plane sensor-processor arrays. Master\u2019s thesis, Imperial College London (2019)"},{"issue":"1","key":"29_CR11","first-page":"6869","volume":"18","author":"I Hubara","year":"2017","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Quantized neural networks: training neural networks with low precision weights and activations. J. Mach. Learn. Res. 18(1), 6869\u20136898 (2017)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"29_CR12","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1109\/JSSC.2003.820876","volume":"39","author":"T Komuro","year":"2004","unstructured":"Komuro, T., Kagami, S., Ishikawa, M.: A dynamically reconfigurable SIMD processor for a vision chip. IEEE J. Solid-State Circuits 39(1), 265\u2013268 (2004)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"29_CR13","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1016\/j.neucom.2017.09.046","volume":"275","author":"S Liang","year":"2018","unstructured":"Liang, S., Yin, S., Liu, S., Luk, W., Wei, S.: FP-BNN: binarized neural network on FPGA. Neurocomputing 275, 1072\u20131086 (2018)","journal-title":"Neurocomputing"},{"issue":"2","key":"29_CR14","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCAS.2018.2821772","volume":"18","author":"A Rodriguez-Vazquez","year":"2018","unstructured":"Rodriguez-Vazquez, A., Fern\u00e1ndez-Berni, J., Le\u00f1ero-Bardallo, J.A., Vornicu, I., Carmona-Gal\u00e1n, R.: CMOS vision sensors: embedding computer vision at imaging front-ends. IEEE Circuits Syst. Mag. 18(2), 90\u2013107 (2018)","journal-title":"IEEE Circuits Syst. Mag."},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Sim, J., Park, J.S., Kim, M., Bae, D., Choi, Y., Kim, L.S.: A 1.42 TOPS\/W deep convolutional neural network recognition processor for intelligent IoE systems. In: 2016 IEEE International Solid-State Circuits Conference (ISSCC), pp. 264\u2013265. IEEE (2016)","DOI":"10.1109\/ISSCC.2016.7418008"},{"key":"29_CR16","unstructured":"Wong, M.: Analog vision - neural network inference acceleration using analog SIMD computation in the focal plane. M.Sc. dissertation, Imperial College London (2018)"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Zhao, R., et al.: Accelerating binarized convolutional neural networks with software-programmable FPGAs, pp. 15\u201324 (02 2017)","DOI":"10.1145\/3020078.3021741"},{"key":"29_CR18","unstructured":"Zhou, A., Yao, A., Guo, Y., Xu, L., Chen, Y.: Incremental network quantization: towards lossless CNNs with low-precision weights. arXiv preprint arXiv:1702.03044 (2017)"},{"key":"29_CR19","unstructured":"Zhu, C., Han, S., Mao, H., Dally, W.J.: Trained ternary quantization. arXiv preprint arXiv:1612.01064 (2016)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58526-6_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:20:18Z","timestamp":1728174018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58526-6_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585259","9783030585266"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58526-6_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","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":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}