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Moreover, energy-harvesting technology-based IoT devices have shown the advantages of green and low-carbon economy, convenient maintenance, and theoretically infinite lifetime, and so on. However, the harvested energy is often unstable, resulting in low performance due to the fact that a fixed load cannot sufficiently utilize the harvested energy. To address this problem, recent works focusing on ReRAM-based convolutional neural networks (CNN) accelerators under harvested energy have proposed hardware\/software optimizations. However, those works have overlooked the mismatch between the power requirement of different CNN layers and the variation of harvested power.<\/jats:p>\n          <jats:p>\n            Motivated by the above observation, this article proposes a novel strategy, called\n            <jats:italic>REC<\/jats:italic>\n            , that retimes convolutional layers of CNN inferences to improve the performance and energy efficiency of energy harvesting ReRAM-based accelerators. Specifically, at the offline stage,\n            <jats:italic>REC<\/jats:italic>\n            defines different power levels to fit the power requirements of different convolutional layers. At runtime, instead of sequentially executing the convolutional layers of an inference one by one,\n            <jats:italic>REC<\/jats:italic>\n            retimes the execution timeframe of different convolutional layers so as to accommodate different CNN layers to the changing power inputs. What is more,\n            <jats:italic>REC<\/jats:italic>\n            provides a parallel strategy to fully utilize very high power inputs. Moreover, a case study is presented to show that\n            <jats:italic>REC<\/jats:italic>\n            is effective to improve the real-time accomplishment of periodical critical inferences because\n            <jats:italic>REC<\/jats:italic>\n            provides an opportunity for critical inferences to preempt the process window with a high power supply. Our experimental results show that the proposed\n            <jats:italic>REC<\/jats:italic>\n            scheme achieves an average performance improvement of 6.1\u00d7 (up to 16.5\u00d7) compared to the traditional strategy without the\n            <jats:italic>REC<\/jats:italic>\n            idea. The case study results show that the\n            <jats:italic>REC<\/jats:italic>\n            scheme can significantly improve the success rate of periodical critical inferences\u2019 real-time accomplishment.\n          <\/jats:p>","DOI":"10.1145\/3652593","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T12:00:45Z","timestamp":1710504045000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["REC: REtime Convolutional Layers to Fully Exploit Harvested Energy for ReRAM-based CNN Accelerators"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8782-9927","authenticated-orcid":false,"given":"Kunyu","family":"Zhou","sequence":"first","affiliation":[{"name":"Capital Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5851-777X","authenticated-orcid":false,"given":"Keni","family":"Qiu","sequence":"additional","affiliation":[{"name":"Capital Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CICSYN.2009.91"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD45719.2019.8942154"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2016.032"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.2977078"},{"issue":"5","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3476992","article-title":"Heterogeneity-aware multicore synchronization for intermittent systems","volume":"20","author":"Chen Wei-Ming","year":"2021","unstructured":"Wei-Ming Chen, Tei-Wei Kuo, and Pi-Cheng Hsiu. 2021. 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