{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:17:05Z","timestamp":1768810625390,"version":"3.49.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T00:00:00Z","timestamp":1790985600000},"content-version":"vor","delay-in-days":303,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Wuxi Science and Technology Development Fund Project","award":["K20241027"],"award-info":[{"award-number":["K20241027"]}]},{"name":"National Science Foundation","award":["61972180"],"award-info":[{"award-number":["61972180"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["JUSRP202501072"],"award-info":[{"award-number":["JUSRP202501072"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Reconfigurable Technol. Syst."],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>\n                    While Deep Neural Networks (DNNs) have achieved remarkable progress in Image Super-Resolution (SR) task, they face significant challenges for edge processing FHD images. Complex DNN operators lead to high hardware resource consumption and latency. Computational inefficiency of FPU increases energy consumption, while DDR access overhead and on-chip memory overflow further constrain real-time capabilities. To address this, we propose ISRLUT, a novel accelerator architecture focused on integer-only inference and near-memory computing. Its core contributions include: (1) Fusion of Neural LUT arithmetic with reconfigurable compute units, transforming unified LUT operators from DNN operators and enhancing hardware utilization; (2) An integer-only inference and parallel architecture, eliminating floating-point dependencies and significantly reducing energy consumption; (3) An innovative internal operator memory management scheme coupled with Tile-based Buffer Overlap and Private Cache Mechanism. We deploy ISRLUT on FPGA and ASIC platforms. Experiments demonstrate that ISRLUT achieves efficient performance: For 4\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    upscaling, it requires only 36.9 KB of storage and achieves a PSNR of 30.21\u2009dB on Set5. Hardware implementation using a 55\u2009nm ASIC consumes merely 0.0337\u2009W power, delivers an energy efficiency of 7278.6 Mpixels\/s\/W, and achieves a real-time frame rate of 118 FPS for 4\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    FHD processing, validating its superiority in energy efficiency and hardware utilization.\n                  <\/jats:p>","DOI":"10.1145\/3770759","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T13:20:11Z","timestamp":1759497611000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["ISRLUT: Integer-Only FHD Image Super-Resolution Based\u00a0on\u00a0Neural Lookup Table and Near-Memory Computing"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9301-4130","authenticated-orcid":false,"given":"Tianshuo","family":"Lu","sequence":"first","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9902-9755","authenticated-orcid":false,"given":"Jianyang","family":"Ding","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8171-9008","authenticated-orcid":false,"given":"Bowen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1364-0976","authenticated-orcid":false,"given":"Huachen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0281-9506","authenticated-orcid":false,"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3822-1653","authenticated-orcid":false,"given":"Zhilei","family":"Chai","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783725"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2023.3261060"},{"key":"e_1_3_1_5_2","unstructured":"Saifuddin Hitawala Yao Li Xian Wang and Dongyang Yang. 2018. Image super-resolution using VDSR-ResNeXt and SRCGAN. arXiv:1810.05731. Retrieved from https:\/\/arxiv.org\/abs\/1810.05731"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2024.3349581"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2024.3425753"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358263"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3547658"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00075"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2020.3014454"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ESSCIRC55480.2022.9911509"},{"key":"e_1_3_1_14_2","first-page":"85340","article-title":"TinyLUT: Tiny look-up table for efficient image restoration at the edge","volume":"37","author":"Li Huanan","year":"2024","unstructured":"Huanan Li, Juntao Guan, Lai Rui, Sijun Ma, and Lin Gu. 2024. TinyLUT: Tiny look-up table for efficient image restoration at the edge. Advances in Neural Information Processing Systems 37 (2024), 85340\u201385359.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19797-0_14"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02458"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2022.3224964"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.151"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01122"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3652855"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO56248.2022.00018"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM62733.2025.00058"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19790-1_19"},{"key":"e_1_3_1_24_2","unstructured":"Mahdi Nazemi Ghasem Pasandi and Massoud Pedram. 2018. NullaNet: Training deep neural networks for reduced-memory-access inference. arXiv:1807.08716. 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