{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:28:23Z","timestamp":1747189703589,"version":"3.40.5"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"DOI":"10.13039\/501100010909","name":"Young Scientists Fund","doi-asserted-by":"publisher","award":["61704136"],"award-info":[{"award-number":["61704136"]}],"id":[{"id":"10.13039\/501100010909","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2018M631163"],"award-info":[{"award-number":["2018M631163"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009996","name":"Shaanxi Province Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017BSHEDZZ29"],"award-info":[{"award-number":["2017BSHEDZZ29"]}],"id":[{"id":"10.13039\/501100009996","id-type":"DOI","asserted-by":"publisher"}]},{"name":"R & D projects in key areas of Guangdong Province","award":["2019B010154002"],"award-info":[{"award-number":["2019B010154002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:p> Pedestrian recognition has achieved the state-of-the-art performance due to the progress of recent convolutional neural network (CNN). However, mainstream CNN models are too complicated to emerging Computing-In-Memory (CIM) architectures for hardware implementation, because enormous parameters and massive intermediate processing results may incur severe \u201cmemory bottleneck\u201d. This paper proposed a design methodology of Parameter Substitution with Nodes Compensation (PSNC) to significantly reduce parameters of CNN model without inference accuracy degradation. Based on the PSNC methodology, an ultra-lightweight convolutional neural network (UL-CNN) was designed. The UL-CNN model is a specially optimized convolutional neural network aiming at a flash-based CIM architecture (Conv-Flash) and to apply for recognizing person. The implementation result of running UL-CNN on Conv-Flash shows that the inference accuracy is up to 94.7%. Compared to LeNet-5, on the premise of the similar operations and accuracy, the amounts of UL-CNN\u2019s parameters are less than 37% of LeNet-5 at the same dataset benchmark. Such parameter reduction can dramatically speed up the training process and economize on-chip storage overhead, as well as save the power consumption of the memory access. With the aid of UL-CNN, the Conv-Flash architecture can provide the best energy efficiency compared to other platforms (CPU, GPU, FPGA, etc.), which consumes only 2.2 \u00d7 105J to complete pedestrian recognition for one frame. <\/jats:p>","DOI":"10.1142\/s0218126621500225","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T05:09:11Z","timestamp":1589864951000},"page":"2150022","source":"Crossref","is-referenced-by-count":4,"title":["UL-CNN: An Ultra-Lightweight Convolutional Neural Network Aiming at Flash-Based Computing-In-Memory Architecture for Pedestrian Recognition"],"prefix":"10.1142","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8221-7670","authenticated-orcid":false,"given":"Chen","family":"Yang","sequence":"first","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Xu","sequence":"additional","affiliation":[{"name":"Hangzhou Flashbillion Semiconductor Co. Ltd, Building 17, No. 57, Science and Technology Park Road, Baiyang District, Hangzhou, Zhejiang 310018, P. R. 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