{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:13:25Z","timestamp":1743005605019,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031398100"},{"type":"electronic","value":"9783031398117"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-39811-7_8","type":"book-chapter","created":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T18:01:28Z","timestamp":1693159288000},"page":"94-108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis of Optimum 3-Dimensional Array and Fast Data Movement for Efficient Memory Computation in Convolutional Neural Network Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0348-6627","authenticated-orcid":false,"given":"Deepika","family":"Selvaraj","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7797-4749","authenticated-orcid":false,"given":"Arunachalam","family":"Venkatesan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5510-4152","authenticated-orcid":false,"given":"David","family":"Novo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,28]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"24411","DOI":"10.1109\/ACCESS.2018.2830661","volume":"6","author":"WG Hatcher","year":"2018","unstructured":"Hatcher, W.G., Yu, W.: A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6, 24411\u201324432 (2018)","journal-title":"IEEE Access"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Moolchandani, D., Kumar, A., Sarangi, S.R.: Accelerating CNN Inference on ASICs: a survey. J. Syst. Architect., preprint, Sept. (2020)","DOI":"10.1016\/j.sysarc.2020.101887"},{"issue":"3","key":"8_CR3","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.eng.2020.01.007","volume":"6","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Xie, Y., Song, L., Chen, F., Tang, T.: A survey of accelerator architectures for deep neural networks. Engineering 6(3), 264\u2013274 (2020)","journal-title":"Engineering"},{"issue":"12","key":"8_CR4","doi-asserted-by":"publisher","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","volume":"105","author":"V Sze","year":"2017","unstructured":"Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295\u20132329 (2017)","journal-title":"Proc. IEEE"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Peemen, M., Setio, A.A., Mesman, B., Corporaal, H.: Memory-centric accelerator design for convolutional neural networks. In: 2013 IEEE 31st International Conference on Computer Design (ICCD), pp. 13\u201319. IEEE (2013)","DOI":"10.1109\/ICCD.2013.6657019"},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/JSSC.2016.2616357","volume":"52","author":"YH Chen","year":"2017","unstructured":"Chen, Y.H., et al.: Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE J. Solid-State Circuits 52, 127\u2013138 (2017)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Moons, B., Uytterhoeven, R., Dehaene, W., Verhelst, M.: 14.5 envision: a 0.26-to-10tops\/w subword-parallel dynamic-voltageaccuracy-frequency-scalable convolutional neural network processor in 28 nm FDSOI. In: 2017 IEEE International Solid-State Circuits Conference (ISSCC), pp. 246\u2013247. IEEE (2017)","DOI":"10.1109\/ISSCC.2017.7870353"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Han, S., et al.: EIE: efficient inference engine on compressed deep neural network. In: Proceedings of the 43rd ACM\/IEEE Annual International Symposium on Computer Architecture (ISCA), Seoul, South Korea, Jun, pp. 243\u2013254 (2016)","DOI":"10.1109\/ISCA.2016.30"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Parashar, A., et al.: SCNN: an accelerator for compressed-sparse convolutional neural networks. In: Proceedings of the ACM\/IEEE 44th Annual International Symposium\u00a0on\u00a0Computer Architecture (ISCA), Jun. 2017, pp. 27\u201340","DOI":"10.1145\/3079856.3080254"},{"issue":"2","key":"8_CR10","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/JSSC.2019.2946771","volume":"55","author":"Z Yuan","year":"2020","unstructured":"Yuan, Z., et al.: STICKER: an energy-efficient multi-sparsity compatible accelerator for convolutional neural networks in 65-nm CMOS. IEEE J. Solid-State Circuits 55(2), 465\u2013477 (2020)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Kang, H.J.: Short floating-point representation for convolutional neural network inference. IEICE Electronics Express, pp. 15-20180909 (2018)","DOI":"10.1587\/elex.15.20180909"},{"issue":"2","key":"8_CR12","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1109\/TC.2021.3051627","volume":"71","author":"S Deepika","year":"2021","unstructured":"Deepika, S., Arunachalam, V.: Analysis & design of convolution operator for high speed and high accuracy convolutional neural network-based inference engines. IEEE Trans. Comput. 71(2), 390\u2013396 (2021)","journal-title":"IEEE Trans. Comput."},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Yue, J., et al.: A 3.77 TOPS\/W convolutional neural network processor with priority-driven kernel optimization. IEEE Trans. Circuits Syst. II, Exp. Briefs 66, 277\u2013281 (2018)","DOI":"10.1109\/TCSII.2018.2846698"},{"issue":"3","key":"8_CR14","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/JETCAS.2020.3015294","volume":"10","author":"G Dinelli","year":"2020","unstructured":"Dinelli, G., Meoni, G., Rapuano, E., Pacini, T., Fanucci, L.: MEM-OPT: a scheduling and data re-use system to optimize on-chip memory usage for CNNs On-Board FPGAs. IEEE J. Emerg. Selected Top. Circ. Syst. 10(3), 335\u2013347 (2020)","journal-title":"IEEE J. Emerg. Selected Top. Circ. Syst."},{"key":"8_CR15","first-page":"1","volume":"2021","author":"YS Chong","year":"2021","unstructured":"Chong, Y.S., Goh, W.L., Ong, Y.S., Nambiar, V.P., Do, A.T.: An energy-efficient convolution unit for depthwise separable convolutional neural networks. IEEE Int. Symp. Circ. Syst. (ISCAS) 2021, 1\u20135 (2021)","journal-title":"IEEE Int. Symp. Circ. Syst. (ISCAS)"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Siu, K., Stuart, D.M., Mahmoud, M., Moshovos, A.: Memory requirements for convolutional neural network hardware accelerators. In: 2018 IEEE International Symposium on Workload Characterization (IISWC), pp. 111\u2013121. IEEE (2018)","DOI":"10.1109\/IISWC.2018.8573527"}],"container-title":["IFIP Advances in Information and Communication Technology","Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39811-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T12:02:35Z","timestamp":1698580955000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39811-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031398100","9783031398117"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39811-7_8","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"28 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCSP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer, Communication, and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chennai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 January 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 January 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icccsp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icccsp.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","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":"123","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":"17","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":"9","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":"14% - 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":"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)"}}]}}