{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:12:29Z","timestamp":1777734749814,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,5]]},"DOI":"10.1145\/3665314.3670837","type":"proceedings-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T19:31:18Z","timestamp":1725910278000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Energy Harvesting-Supported Efficient Low-Power ML Processing with Adaptive Checkpointing and Intermittent Computing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1861-249X","authenticated-orcid":false,"given":"Sanket","family":"Shukla","sequence":"first","affiliation":[{"name":"George mason university, Fairfax, VA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4417-2387","authenticated-orcid":false,"given":"Sai Manoj","family":"Pudukotai Dinakarrao","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, VA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Davide Brunelli, Bashir M. Al-Hashimi, Geoff V. Merrett, and Luca Benini.","author":"Balsamo Domenico","year":"2016","unstructured":"Domenico Balsamo, Alex S. Weddell, Anup Das, Alberto Rodr\u00edguez Arreola, Davide Brunelli, Bashir M. Al-Hashimi, Geoff V. Merrett, and Luca Benini. 2016. Hibernus++: A Self-Calibrating and Adaptive System for Transiently-Powered Embedded Devices. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2016)."},{"key":"e_1_3_2_1_2_1","volume-title":"Hibernus: Sustaining Computation During Intermittent Supply for Energy-Harvesting Systems","author":"Balsamo Domenico","year":"2015","unstructured":"Domenico Balsamo, Alex S. Weddell, Geoff V. Merrett, Bashir M. Al-Hashimi, Davide Brunelli, and Luca Benini. 2015. Hibernus: Sustaining Computation During Intermittent Supply for Energy-Harvesting Systems. IEEE Embedded Systems Letters (2015)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983990.2983995"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.23919\/DATE51398.2021.9474036"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Zahra Ghodsi Siddharth Garg and Ramesh Karri. 2017. Optimal checkpointing for secure intermittently-powered IoT devices. (2017).","DOI":"10.1109\/ICCAD.2017.8203802"},{"key":"e_1_3_2_1_6_1","volume-title":"Azana Hafizah Mohd Aman","author":"Hassan Rosilah","year":"2020","unstructured":"Rosilah Hassan, Faizan Qamar, Mohammad Kamrul Hasan, Azana Hafizah Mohd Aman, and Amjed Sid Ahmed. 2020. Internet of Things and Its Applications: A Comprehensive Survey. Symmetry (2020)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080238"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Hrishikesh Jayakumar Arnab Raha Jacob R. Stevens and Vijay Raghunathan. 2017. Energy-Aware Memory Mapping for Hybrid FRAM-SRAM MCUs in Intermittently-Powered IoT Devices. (2017).","DOI":"10.1145\/2983628"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3526241.3530825"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3369837"},{"key":"e_1_3_2_1_12_1","unstructured":"Ji Lin Wei-Ming Chen Han Cai Chuang Gan and Song Han. 2021. MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning. (2021)."},{"key":"e_1_3_2_1_13_1","volume-title":"Enabling Technologies, Security and Privacy, and Applications","author":"Lin Jie","year":"2017","unstructured":"Jie Lin, Wei Yu, Nan Zhang, Xinyu Yang, Hanlin Zhang, and Wei Zhao. 2017. A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"Intermittent computing: Challenges and opportunities. Summit on Advances in Programming Languages","author":"Lucia Brandon","year":"2017","unstructured":"Brandon Lucia, Vignesh Balaji, Alexei Colin, Kiwan Maeng, and Emily Ruppel. 2017. Intermittent computing: Challenges and opportunities. Summit on Advances in Programming Languages (2017)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2737924.2737978"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Benjamin Ransford Jacob M. Sorber and Kevin Fu. 2011. Mementos: system support for long-running computation on RFID-scale devices. (2011).","DOI":"10.1145\/1950365.1950386"},{"key":"e_1_3_2_1_17_1","unstructured":"Muhammad Moid Sandhu Kai Geissdoerfer Sara Khalifa Raja Jurdak Marius Portmann and Brano Kusy. 2020. Towards optimal kinetic energy harvesting for the batteryless IoT. (2020)."},{"key":"e_1_3_2_1_18_1","volume-title":"Sarmad Abbas, Lukas Sekanina, Zdenek Vasicek, and Vojtech Mrazek.","author":"Shafique Muhammad","year":"2017","unstructured":"Muhammad Shafique, Rehan Hafiz, Muhammad Usama Javed, Sarmad Abbas, Lukas Sekanina, Zdenek Vasicek, and Vojtech Mrazek. 2017. Adaptive and energy-efficient architectures for machine learning: Challenges, opportunities, and research roadmap. (2017)."},{"key":"e_1_3_2_1_19_1","volume-title":"International Symposium on Quality Electronic Design (ISQED).","author":"Shukla Sanket","year":"2024","unstructured":"Sanket Shukla and Sai Manoj Pudukottai Dinakarrao. 2024. Bring it On: Kinetic Energy Harvesting to Spark Machine Learning Computations in IoTs. In International Symposium on Quality Electronic Design (ISQED)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Sanket Shukla Setareh Rafatirad Houman Homayoun and Sai Manoj Pudukottai Dinakarrao. 2023. Federated Learning with Heterogeneous Models for On-device Malware Detection in IoT Networks. (2023).","DOI":"10.23919\/DATE56975.2023.10137288"},{"key":"e_1_3_2_1_21_1","volume-title":"Shubhankar Suman Singh, and Smruti R. Sarangi","author":"Singla Priyanka","year":"2019","unstructured":"Priyanka Singla, Shubhankar Suman Singh, and Smruti R. Sarangi. 2019. Flexi-Check: An Adaptive Checkpointing Architecture for Energy Harvesting Devices, (DATE). (2019)."},{"key":"e_1_3_2_1_22_1","volume-title":"Le","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc V. Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ArXiv (2019)."},{"key":"e_1_3_2_1_23_1","volume-title":"Branchynet: Fast inference via early exiting from deep neural networks.","author":"Teerapittayanon Surat","year":"2016","unstructured":"Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. 2016. Branchynet: Fast inference via early exiting from deep neural networks. (2016)."},{"key":"e_1_3_2_1_24_1","unstructured":"Joel Van Der Woude and Matthew Hicks. 2016. Intermittent Computation without Hardware Support or Programmer Intervention. (2016)."},{"key":"e_1_3_2_1_25_1","volume-title":"Merrett","author":"Verykios Theodoros D.","year":"2019","unstructured":"Theodoros D. Verykios, Domenico Balsamo, and Geoff V. Merrett. 2019. Selective policies for efficient state retention in transiently-powered embedded systems: Exploiting properties of NVM technologies. Sustainable Computing: Informatics and Systems (2019)."},{"key":"e_1_3_2_1_26_1","volume-title":"Zero time waste: Recycling predictions in early exit neural networks. Neural Information Processing Systems","author":"Wo\u0142czyk Maciej","year":"2021","unstructured":"Maciej Wo\u0142czyk, Bartosz W\u00f3jcik, Klaudia Ba\u0142azy, Igor T Podolak, Jacek Tabor, Marek \u015amieja, and Tomasz Trzcinski. 2021. Zero time waste: Recycling predictions in early exit neural networks. Neural Information Processing Systems (2021)."},{"key":"e_1_3_2_1_27_1","volume-title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Conference on Computer Vision and Pattern Recognition","author":"Zhang Xiangyu","year":"2017","unstructured":"Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2017. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Conference on Computer Vision and Pattern Recognition (2017)."}],"event":{"name":"ISLPED '24: 29th ACM\/IEEE International Symposium on Low Power Electronics and Design","location":"Newport Beach CA USA","acronym":"ISLPED '24","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CAS","IEEE EDA"]},"container-title":["Proceedings of the 29th ACM\/IEEE International Symposium on Low Power Electronics and Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3665314.3670837","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3665314.3670837","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:57:51Z","timestamp":1750294671000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3665314.3670837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,5]]},"references-count":27,"alternative-id":["10.1145\/3665314.3670837","10.1145\/3665314"],"URL":"https:\/\/doi.org\/10.1145\/3665314.3670837","relation":{},"subject":[],"published":{"date-parts":[[2024,8,5]]},"assertion":[{"value":"2024-09-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}