{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:27:44Z","timestamp":1781018864619,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"funder":[{"name":"NSERC Discovery Grant","award":["RGPIN 2025-00129"],"award-info":[{"award-number":["RGPIN 2025-00129"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,3,23]]},"DOI":"10.1145\/3748522.3779788","type":"proceedings-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:17:49Z","timestamp":1781014669000},"page":"1164-1171","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4931-7207","authenticated-orcid":false,"given":"Atah","family":"Nuh Mih","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, University of New Brunswick, Fredericton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2833-1598","authenticated-orcid":false,"given":"Jianzhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of New Brunswick, Fredericton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6750-9536","authenticated-orcid":false,"given":"Hung Truong Thanh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of New Brunswick, Fredericton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0788-4377","authenticated-orcid":false,"given":"Hung","family":"Cao","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of New Brunswick, Fredericton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Learning Representations.","author":"Bowen","unstructured":"Bowen Baker et al. 2018. Accelerating neural architecture search using performance prediction. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_2_1","volume-title":"IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 1493\u20131502","author":"Nicco\u00ec","unstructured":"Nicco\u00ec O Cavagnero et al. 2023. Freerea: training-free evolution-based architecture search. In IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 1493\u20131502."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Angelica Chen et al. 2023. Evoprompting: language models for code-level neural architecture search. Advances in Neural Information Processing Systems 36.","DOI":"10.52202\/075280-0342"},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Learning Representations.","author":"Wuyang","unstructured":"Wuyang Chen et al. 2021. Neural architecture search on imagenet in four gpu hours: a theoretically inspired perspective. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3253818"},{"key":"e_1_3_2_1_6_1","unstructured":"Patryk Chrabaszcz et al. 2017. A downsampled variant of imagenet as an alternative to the cifar datasets. (2017). arXiv: 1707.08819 [cs.CV]."},{"key":"e_1_3_2_1_7_1","article-title":"Nats-bench: benchmarking nas algorithms for architecture topology and size","author":"Xuanyi Dong","year":"2022","unstructured":"Xuanyi Dong et al. 2022. Nats-bench: benchmarking nas algorithms for architecture topology and size. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, (July 2022), 3634\u20133646, 7, (July 2022).","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence, 44"},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Learning Representations.","author":"Dong Xuanyi","year":"2020","unstructured":"Xuanyi Dong and Yi Yang. 2020. Nas-bench-201: extending the scope of reproducible neural architecture search. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 20","author":"Yanjie","unstructured":"Yanjie Gao et al. 2020. Estimating gpu memory consumption of deep learning models. Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 20."},{"key":"e_1_3_2_1_10_1","volume-title":"Asian Conference on Machine Learning. PMLR, 374\u2013389","author":"Eddine Mohamed Imed","unstructured":"Mohamed Imed Eddine Ghebriout et al. 2024. Harmonic-nas: hardware-aware multimodal neural architecture search on resource-constrained devices. In Asian Conference on Machine Learning. PMLR, 374\u2013389."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 1638\u20131647","author":"Amir","unstructured":"Amir Gholami et al. 2018. Squeezenext: hardware-aware neural network design. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 1638\u20131647."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition.","author":"Minghao","unstructured":"Minghao Guo et al. 2019. Irlas: inverse reinforcement learning for architecture search. In Proceedings of the IEEE conference on computer vision and pattern recognition."},{"key":"e_1_3_2_1_13_1","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images."},{"key":"e_1_3_2_1_14_1","volume-title":"IEEE International Conference on Edge Computing and Communications.","author":"Achintya","unstructured":"Achintya Kundu et al. 2023. Transfer-once-for-all: ai model optimization for edge. IEEE International Conference on Edge Computing and Communications."},{"key":"e_1_3_2_1_15_1","volume-title":"ICLR 2021 - 9th International Conference on Learning Representations, (Mar.","author":"Chaojian","year":"2021","unstructured":"Chaojian Li et al. 2021. Hw-nas-bench:hardware-aware neural architecture search benchmark. ICLR 2021 - 9th International Conference on Learning Representations, (Mar. 2021)."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis.","author":"Yuke","unstructured":"Yuke Li et al. 2023. Pareto optimization of cnn models via hardware-aware neural architecture search for drainage crossing classification on resource-limited devices. Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis."},{"key":"e_1_3_2_1_17_1","volume-title":"European Conference on Computer Vision (ECCV).","author":"Chenxi","unstructured":"Chenxi Liu et al. 2018. Progressive neural architecture search. In European Conference on Computer Vision (ECCV)."},{"key":"e_1_3_2_1_18_1","unstructured":"Renqian Luo et al. 2021. Neural architecture optimization. In Neural Information Processing Systems. https:\/\/github.com\/renqianluo\/NAO.."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors","author":"Xiangzhong","year":"2020","unstructured":"Xiangzhong Luo et al. 2020. Edgenas: discovering efficient neural architectures for edge systems. Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors, 2020-October, (Oct. 2020), 288\u2013295."},{"key":"e_1_3_2_1_20_1","first-page":"1","article-title":"Resource-constrained neural architecture search on edge devices","volume":"9","author":"Bo Lyu","year":"2022","unstructured":"Bo Lyu et al. 2022. Resource-constrained neural architecture search on edge devices. IEEE Transactions on Network Science and Engineering, 9, 1.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_2_1_21_1","volume-title":"International conference on machine learning. PMLR, 7588\u20137598","author":"Joe","unstructured":"Joe Mellor et al. 2021. Neural architecture search without training. In International conference on machine learning. PMLR, 7588\u20137598."},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the Canadian Conference on Artificial Intelligence.","author":"Nuh Atah","unstructured":"Atah Nuh Mih et al. 2024. Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment. Proceedings of the Canadian Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_23_1","volume-title":"International conference on machine learning. PMLR.","author":"Hieu","unstructured":"Hieu Pham et al. 2018. Efficient neural architecture search via parameters sharing. In International conference on machine learning. PMLR."},{"key":"e_1_3_2_1_24_1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Colin Raffel","year":"2020","unstructured":"Colin Raffel et al. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21, 140, 1\u201367.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence.","author":"Esteban","unstructured":"Esteban Real et al. 2019. Regularized evolution for image classifier architecture search. Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_26_1","first-page":"1","article-title":"A comprehensive survey of neural architecture search: challenges and solutions","volume":"54","author":"Pengzhen Ren","year":"2021","unstructured":"Pengzhen Ren et al. 2021. A comprehensive survey of neural architecture search: challenges and solutions. ACM Computing Surveys (CSUR), 54, 4, 1\u201334.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Blake Richey et al. 2024. Multi-reward optimization using genetic algorithms for edge ai. In Real-Time Image Processing and Deep Learning 2024. SPIE.","DOI":"10.1117\/12.3013331"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","unstructured":"Matteo Risso et al. 2022. Lightweight neural architecture search for temporal convolutional networks at the edge. 10.1109\/TC.2022.3177955","DOI":"10.1109\/TC.2022.3177955"},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 2628\u20132637","author":"Nilotpal","unstructured":"Nilotpal Sinha et al. 2024. Hardware aware evolutionary neural architecture search using representation similarity metric. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 2628\u20132637."},{"key":"e_1_3_2_1_30_1","volume-title":"IEEE\/CVF conference on computer vision and pattern recognition.","author":"Bichen","unstructured":"Bichen Wu et al. 2019. Fbnet: hardware-aware efficient convnet design via differentiable neural architecture search. In IEEE\/CVF conference on computer vision and pattern recognition."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.154"},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Mobile Systems, Applications, and Services. ACM.","author":"Lyna Li","unstructured":"Li Lyna Zhang et al. 2021. Towards accurate latency prediction of deep-learning model inference on diverse edge devices. In International Conference on Mobile Systems, Applications, and Services. ACM."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Yusen Zhang et al. 2024. Oncenas: discovering efficient on-device inference neural networks for edge devices. Information Sciences.","DOI":"10.1016\/j.ins.2024.120567"},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition, 8697\u20138710","author":"Barret","unstructured":"Barret Zoph et al. 2018. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 8697\u20138710."},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Learning Representations.","author":"Zoph Barret","year":"2017","unstructured":"Barret Zoph and Quoc Le. 2017. Neural architecture search with reinforcement learning. In International Conference on Learning Representations."}],"event":{"name":"SAC '26: 41st ACM\/SIGAPP Symposium on Applied Computing","location":"Grand Hotel Palace Thessaloniki Greece","acronym":"SAC '26","sponsor":["SIGAPP ACM Special Interest Group on Applied Computing"]},"container-title":["Proceedings of the 41st ACM\/SIGAPP Symposium on Applied Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3748522.3779788","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:37:19Z","timestamp":1781015839000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3748522.3779788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":35,"alternative-id":["10.1145\/3748522.3779788","10.1145\/3748522"],"URL":"https:\/\/doi.org\/10.1145\/3748522.3779788","relation":{},"subject":[],"published":{"date-parts":[[2026,3,23]]},"assertion":[{"value":"2026-06-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}