{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T04:10:24Z","timestamp":1781496624206,"version":"3.54.1"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"23","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,12,1]]},"DOI":"10.1109\/jiot.2025.3613700","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T17:34:22Z","timestamp":1758735262000},"page":"51198-51208","source":"Crossref","is-referenced-by-count":3,"title":["A Systematic Framework for Compressing Generative Diffusion Models for Resource-Constrained IoT Devices"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5804-276X","authenticated-orcid":false,"given":"Zhenquan","family":"Qin","sequence":"first","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2864-9244","authenticated-orcid":false,"given":"Sen","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-6539","authenticated-orcid":false,"given":"Bingxian","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6921-7369","authenticated-orcid":false,"given":"Guangjie","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Maritime Intelligent Network Information Technology, Ministry of Education, Hohai University, Changzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"},{"key":"ref3","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref4","article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Song"},{"key":"ref5","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. 9th Int. Conf. Learn. Represent.","author":"Song"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"ref8","first-page":"32947","article-title":"DiMSUM: Diffusion mamba\u2014A scalable and unified spatial-frequency method for image generation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"37","author":"Phung"},{"key":"ref9","article-title":"Video generation models as world simulators","author":"Brooks","year":"2024"},{"key":"ref10","first-page":"5775","article-title":"DPM-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"L\u00fc"},{"key":"ref11","first-page":"32145","article-title":"Consistency models","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","volume":"202","author":"Song"},{"key":"ref12","article-title":"Flow matching for generative modeling","author":"Lipman","year":"2022","journal-title":"arXiv:2210.02747"},{"key":"ref13","article-title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","author":"Liu","year":"2022","journal-title":"arXiv:2209.03003"},{"key":"ref14","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref15","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref16","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","volume-title":"arXiv:1510.00149","author":"Han"},{"key":"ref17","first-page":"16716","article-title":"Structural pruning for diffusion models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fang"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49660.2025.10890532"},{"key":"ref19","article-title":"Effortless efficiency: Low-cost pruning of diffusion models","author":"Zhang","year":"2024","journal-title":"arXiv:2412.02852"},{"key":"ref20","article-title":"A simple and effective pruning approach for large language models","author":"Sun","year":"2023","journal-title":"arXiv:2306.11695"},{"key":"ref21","article-title":"Accurate neural network pruning requires rethinking sparse optimization","author":"Kuznedelev","year":"2023","journal-title":"arXiv:2308.02060"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.178"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00196"},{"key":"ref24","article-title":"Q-DiT: Accurate post-training quantization for diffusion transformers","author":"Chen","year":"2024","journal-title":"arXiv:2406.17343"},{"key":"ref25","first-page":"38087","article-title":"SmoothQuant: Accurate and efficient post-training quantization for large language models","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Xiao"},{"key":"ref26","article-title":"AWQ: Activation-aware weight quantization for llm compression and acceleration","author":"Lin","year":"2024","journal-title":"arXiv:2306.00978"},{"key":"ref27","article-title":"OmniQuant: Omnidirectionally calibrated quantization for large language models","author":"Shao","year":"2023","journal-title":"arXiv:2308.13137"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref30","article-title":"Instance normalization: The missing ingredient for fast stylization","author":"Ulyanov","year":"2016","journal-title":"arXiv:1607.08022"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref32","first-page":"16344","article-title":"FlashAttention: Fast and memory-efficient exact attention with IO-awareness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dao"},{"key":"ref33","article-title":"FlashAttention-2: Faster attention with better parallelism and work partitioning","volume-title":"Proc. 12th Int. Conf. Learn. Represent. (ICLR)","author":"Dao"},{"key":"ref34","first-page":"6626","article-title":"Gans trained by a two time-scale update rule converge to a local Nash equilibrium","volume-title":"Proc. NIPS","author":"Heusel"},{"key":"ref35","first-page":"1135","article-title":"Learning both weights and connections for efficient neural networks","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref36","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref37","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11261327\/11177155.pdf?arnumber=11177155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:43:33Z","timestamp":1763664213000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11177155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":37,"journal-issue":{"issue":"23"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3613700","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,1]]}}}