{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T06:29:29Z","timestamp":1759991369172,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954697"},{"type":"electronic","value":"9783030954703"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-95470-3_32","type":"book-chapter","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:07:13Z","timestamp":1643710033000},"page":"423-435","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cascaded Classifier for Pareto-Optimal Accuracy-Cost Trade-Off Using Off-the-Shelf ANNs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6536-820X","authenticated-orcid":false,"given":"Cecilia","family":"Latotzke","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0966-8918","authenticated-orcid":false,"given":"Johnson","family":"Loh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1583-3411","authenticated-orcid":false,"given":"Tobias","family":"Gemmeke","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"32_CR1","unstructured":"ImageNet. https:\/\/www.image-net.org\/. Accessed 01 Feb 2021"},{"key":"32_CR2","doi-asserted-by":"publisher","unstructured":"Badami, K., Lauwereins, S., Meert, W., Verhelst, M.: Context-aware hierarchical information-sensing in a 6$$\\upmu $$W 90nm CMOS voice activity detector. In: ISSCC, vol. 58, pp. 430\u2013432 (2015). https:\/\/doi.org\/10.1109\/ISSCC.2015.7063110","DOI":"10.1109\/ISSCC.2015.7063110"},{"key":"32_CR3","doi-asserted-by":"publisher","unstructured":"Benbasat, A.Y., Paradiso, J.A.: A framework for the automated generation of power-efficient classifiers for embedded sensor nodes. In: SenSys, vol. 5, pp. 219\u2013232 (2007). https:\/\/doi.org\/10.1145\/1322263.1322285","DOI":"10.1145\/1322263.1322285"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Bergstra, J., Yamins, D., Cox, D.D.: Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference, vol. 13, pp. 13\u201319 (2013)","DOI":"10.25080\/Majora-8b375195-003"},{"key":"32_CR5","unstructured":"Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications. CoRR, abs\/1605.07678 (2017)"},{"key":"32_CR6","doi-asserted-by":"publisher","unstructured":"Coca\u00f1a-Fern\u00e1ndez, A., Ranilla, J., Gil-Pita, R., S\u00e1nchez, L.: Energy-conscious fuzzy rule-based classifiers for battery operated embedded devices. In: FUZZ-IEEE, pp. 1\u20136 (2017). https:\/\/doi.org\/10.1109\/FUZZ-IEEE.2017.8015483","DOI":"10.1109\/FUZZ-IEEE.2017.8015483"},{"key":"32_CR7","unstructured":"Courbariaux, M., Bengio, Y.: BinaryNet: training deep neural networks with weights and activations constrained to +1 or -1. CoRR, abs\/1602.02830 (2016)"},{"key":"32_CR8","doi-asserted-by":"publisher","unstructured":"Goens, A., Brauckmann, A., Ertel, S., Cummins, C., Leather, H., Castrillon, J.: A case study on machine learning for synthesizing benchmarks. In: MAPL, pp. 38\u201346 (2019). https:\/\/doi.org\/10.1145\/3315508.3329976","DOI":"10.1145\/3315508.3329976"},{"key":"32_CR9","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1109\/JETCAS.2018.2839347","volume":"8","author":"K Goetschalckx","year":"2018","unstructured":"Goetschalckx, K., Moons, B., Lauwereins, S., Andraud, M., Verhelst, M.: Optimized hierarchical cascaded processing. JETCAS 8, 884\u2013894 (2018). https:\/\/doi.org\/10.1109\/JETCAS.2018.2839347","journal-title":"JETCAS"},{"key":"32_CR10","doi-asserted-by":"publisher","unstructured":"Horowitz, M.: 1.1 Computing\u2019s energy problem (and what we can do about it). In: ISSCC, pp. 10\u201314 (2014). https:\/\/doi.org\/10.1109\/ISSCC.2014.6757323","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"32_CR11","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 2261\u20132269 (2016). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"32_CR12","doi-asserted-by":"publisher","unstructured":"Joshi, A.J., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: CVPR, pp. 2372\u20132379 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206627","DOI":"10.1109\/CVPR.2009.5206627"},{"key":"32_CR13","doi-asserted-by":"publisher","unstructured":"Kouris, A., Venieris, S.I., Bouganis, C.: Cascade CNN: pushing the performance limits of quantisation in convolutional neural networks. In: FPL, pp. 155\u20131557 (2018). https:\/\/doi.org\/10.1109\/FPL.2018.00034","DOI":"10.1109\/FPL.2018.00034"},{"key":"32_CR14","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: CIFAR-10 (Canadian Institute for Advanced Research). http:\/\/www.cs.toronto.edu\/~kriz\/cifar.html"},{"key":"32_CR15","doi-asserted-by":"publisher","first-page":"9785","DOI":"10.1109\/ACCESS.2021.3050670","volume":"9","author":"C Latotzke","year":"2021","unstructured":"Latotzke, C., Gemmeke, T.: Efficiency versus accuracy: a review of design techniques for DNN hardware accelerators. IEEE Access 9, 9785\u20139799 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3050670","journal-title":"IEEE Access"},{"issue":"11","key":"32_CR16","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"32_CR17","unstructured":"Lecun, Y., Cortes, C., Burges, C.: The MNIST Database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"32_CR18","doi-asserted-by":"publisher","unstructured":"Li, L., Topkara, U., Coskun, B., Memon, N.: CoCoST: a computational cost efficient classifier. In: ICDM, pp. 268\u2013277 (2009). https:\/\/doi.org\/10.1109\/ICDM.2009.46","DOI":"10.1109\/ICDM.2009.46"},{"key":"32_CR19","unstructured":"Lin, Z., Memisevic, R., Konda, K.R.: How far can we go without convolution: improving fully-connected networks. CoRR, abs\/1511.02580 (2015)"},{"key":"32_CR20","doi-asserted-by":"publisher","unstructured":"Ouali, M., King, R.D.: Cascaded multiple classifiers for secondary structure prediction. Protein Sci., 1162\u20131176 (2000). https:\/\/doi.org\/10.1110\/ps.9.6.1162","DOI":"10.1110\/ps.9.6.1162"},{"key":"32_CR21","doi-asserted-by":"publisher","unstructured":"Price, M., Glass, J., Chandrakasan, A.P.: A scalable speech recognizer with deep-neural-network acoustic models and voice-activated power gating. In: ISSCC, pp. 244\u2013245 (2017). https:\/\/doi.org\/10.1109\/ISSCC.2017.7870352","DOI":"10.1109\/ISSCC.2017.7870352"},{"key":"32_CR22","doi-asserted-by":"publisher","unstructured":"Rossi, D., et al.: 4.4 A 1.3TOPS\/W @ 32GOPS fully integrated 10-core SoC for IoT end-nodes with 1.7$$\\upmu $$W cognitive wake-up from MRAM-based state-retentive sleep mode. In: ISSCC, vol. 64, pp. 60\u201362 (2021). https:\/\/doi.org\/10.1109\/ISSCC42613.2021.9365939","DOI":"10.1109\/ISSCC42613.2021.9365939"},{"key":"32_CR23","doi-asserted-by":"publisher","unstructured":"Stadtmann, T., Latotzke, C., Gemmeke, T.: From quantitative analysis to synthesis of efficient binary neural networks. In: ICMLA, vol. 19, pp. 93\u2013100 (2020). https:\/\/doi.org\/10.1109\/ICMLA51294.2020.00024","DOI":"10.1109\/ICMLA51294.2020.00024"},{"key":"32_CR24","doi-asserted-by":"publisher","unstructured":"Venkataramani, S., Raghunathan, A., Liu, J., Shoaib, M.: Scalable-effort classifiers for energy-efficient machine learning. In: DAC, vol. 67, pp. 1\u20136 (2015). https:\/\/doi.org\/10.1145\/2744769.2744904","DOI":"10.1145\/2744769.2744904"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Xu, Z., Kusner, M., Weinberger, K., Chen, M.: Cost-sensitive tree of classifiers. In: PMLR, vol. 28, no. 1, pp. 133\u2013141 (2013)","DOI":"10.1609\/aaai.v28i1.8967"},{"issue":"1","key":"32_CR26","first-page":"2113","volume":"15","author":"Z Xu","year":"2014","unstructured":"Xu, Z., Kusner, M.J., Weinberger, K.Q., Chen, M., Chapelle, O.: Classifier cascades and trees for minimizing feature evaluation cost. JMLR 15(1), 2113\u20132144 (2014)","journal-title":"JMLR"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95470-3_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T11:57:57Z","timestamp":1674647877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95470-3_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954697","9783030954703"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95470-3_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","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":"mod2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2021.icas.cc\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"215","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":"86","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":"40% - 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":"5-6","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":"1-2","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)"}}]}}