{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:28:22Z","timestamp":1763202502504,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811611025"},{"type":"electronic","value":"9789811611032"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-981-16-1103-2_22","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T08:03:32Z","timestamp":1616659412000},"page":"249-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["L2PF - Learning to Prune Faster"],"prefix":"10.1007","author":[{"given":"Manoj-Rohit","family":"Vemparala","sequence":"first","affiliation":[]},{"given":"Nael","family":"Fasfous","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Frickenstein","sequence":"additional","affiliation":[]},{"given":"Mhd Ali","family":"Moraly","sequence":"additional","affiliation":[]},{"given":"Aquib","family":"Jamal","sequence":"additional","affiliation":[]},{"given":"Lukas","family":"Frickenstein","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Unger","sequence":"additional","affiliation":[]},{"given":"Naveen-Shankar","family":"Nagaraja","sequence":"additional","affiliation":[]},{"given":"Walter","family":"Stechele","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"unstructured":"Chou, P., Maturana, D., Scherer, S.: Improving stochastic policy gradients in continuous control with deep reinforcement learning using the beta distribution. In: ICML (2017)","key":"22_CR1"},{"doi-asserted-by":"crossref","unstructured":"Fasfous, N., Vemparala, M.R., Frickenstein, A., Stechele, W.: OrthrusPE: runtime reconfigurable processing elements for binary neural networks. In: DATE, Grenoble, France (2020)","key":"22_CR2","DOI":"10.23919\/DATE48585.2020.9116308"},{"doi-asserted-by":"crossref","unstructured":"Frickenstein, A., et al.: ALF: Autoencoder-based low-rank filter-sharing for efficient convolutional neural networks. In: ICML (2020)","key":"22_CR3","DOI":"10.1109\/DAC18072.2020.9218501"},{"doi-asserted-by":"crossref","unstructured":"Frickenstein, A., et al.: Binary DAD-Net: binarized driveable area detection network for autonomous driving. In: ICRA (2020)","key":"22_CR4","DOI":"10.1109\/ICRA40945.2020.9197119"},{"doi-asserted-by":"crossref","unstructured":"Frickenstein, A., Vemparala, M.R., Unger, C., Ayar, F., Stechele, W.: DSC: dense-sparse convolution for vectorized inference of convolutional neural networks. In: CVPR-W (2019)","key":"22_CR5","DOI":"10.1109\/CVPRW.2019.00175"},{"unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: NIPS (2015)","key":"22_CR6"},{"unstructured":"Hasselt, H.V., Guez, A., Hessel, M., Mnih, V., Silver, D.: Learning values across many orders of magnitude. In: NeurIPS (2016)","key":"22_CR7"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","key":"22_CR8","DOI":"10.1109\/CVPR.2016.90"},{"key":"22_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/978-3-030-01234-2_48","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y He","year":"2018","unstructured":"He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., Han, S.: AMC: AutoML for model compression and acceleration on mobile devices. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 815\u2013832. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_48"},{"doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","key":"22_CR10","DOI":"10.1109\/CVPR.2019.00447"},{"doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: ICCV (2017)","key":"22_CR11","DOI":"10.1109\/ICCV.2017.155"},{"doi-asserted-by":"crossref","unstructured":"Huang, Q., Zhou, S.K., You, S., Neumann, U.: Learning to prune filters in convolutional neural networks. In: WACV (2018)","key":"22_CR12","DOI":"10.1109\/WACV.2018.00083"},{"unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10 (Canadian institute for advanced research). http:\/\/www.cs.toronto.edu\/~kriz\/cifar.html","key":"22_CR13"},{"unstructured":"Mohamed, S., Rosca, M., Figurnov, M., Mnih, A.: Monte Carlo gradient estimation in machine learning. CoRR abs\/1906.10652 (2019)","key":"22_CR14"},{"issue":"1","key":"22_CR15","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"Q Ning","year":"1999","unstructured":"Ning, Q.: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145\u2013151 (1999)","journal-title":"Neural Netw."},{"key":"22_CR16","series-title":"Adaptive Computation and Machine Learning Series","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton, R.S., Barto, A.-G.: Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning Series. MIT Press, Cambridge (2018)"},{"unstructured":"Sutton, R., McAllester, D., Singh, S., Mansour, Y.: Policy gradient methods for reinforcement learning with function approximation. In: NIPS (1999)","key":"22_CR17"},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1007\/978-3-030-18656-2_18","volume-title":"Architecture of Computing Systems","author":"MR Vemparala","year":"2019","unstructured":"Vemparala, M.R., Frickenstein, A., Stechele, W.: An efficient FPGA accelerator design for optimized CNNs using OpenCL. In: Schoeberl, M., Hochberger, C., Uhrig, S., Brehm, J., Pionteck, T. (eds.) ARCS 2019. LNCS, vol. 11479, pp. 236\u2013249. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-18656-2_18"},{"issue":"3\u20134","key":"22_CR19","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/BF00992696","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3\u20134), 229\u2013256 (1992). https:\/\/doi.org\/10.1007\/BF00992696","journal-title":"Mach. Learn."},{"key":"22_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-030-01249-6_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T-J Yang","year":"2018","unstructured":"Yang, T.-J., et al.: NetAdapt: platform-aware neural network adaptation for mobile applications. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 289\u2013304. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_18"},{"doi-asserted-by":"crossref","unstructured":"Ye, S., et al.: Adversarial robustness vs. model compression, or both? In: ICCV (2019)","key":"22_CR21","DOI":"10.1109\/ICCV.2019.00020"},{"unstructured":"Zhang, T., et al.: ADAM-ADMM: a unified, systematic framework of structured weight pruning for DNNs. CoRR abs\/1807.11091 (2018)","key":"22_CR22"},{"doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization (2015)","key":"22_CR23","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-1103-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T04:16:29Z","timestamp":1619237789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-1103-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811611025","9789811611032"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-1103-2_22","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prayagraj","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cvip2020.iiita.ac.in","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"352","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":"134","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":"38% - 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)"}},{"value":"Due to the COVID-19 pandemic the conference was partially held in a virtual mode.","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)"}}]}}