{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T04:35:29Z","timestamp":1771043729498,"version":"3.50.1"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2022,9,15]]},"abstract":"<jats:p> In recent decades, convolutional neural network (CNN) has become essential in many real-time applications due to its massive computational ability. But its use in portable devices is limited due to its high computation requirements. This paper proposes a novel One-Pass Processing Element (OPPE) to mitigate this limitation. The proposed OPPE removes redundant computations by eliminating those with zeros that leads to low area as well as low power consumption. The proposed OPPE model is evaluated with the help of VGG-16-based CNN accelerator. The proposed OPPE design reduces the number of four-input LUTs by 5.19%, 15.91%, 10.06% and 4.93% and the power consumption by 4.26%, 7.36%, 5.81% and 1.55% when compared with the conventional processing element (PE), activation gating PE, weight gating PE and zero gating PE, respectively. The proposed CNN accelerator design using OPPE achieves high throughput with less resource utilization. <\/jats:p>","DOI":"10.1142\/s0218126622502267","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T07:11:56Z","timestamp":1653981116000},"source":"Crossref","is-referenced-by-count":14,"title":["High-Throughput and Power-Efficient Convolutional Neural Network Using One-Pass Processing Elements"],"prefix":"10.1142","volume":"31","author":[{"given":"B.","family":"Sivasankari","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, SNS College of Technology, Coimbatore 641035, Tamil Nadu, India"}]},{"given":"M.","family":"Shunmugathammal","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai 600089, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9820-7058","authenticated-orcid":false,"given":"Ahilan","family":"Appathurai","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, Tamil Nadu, India"}]},{"given":"M.","family":"Kavitha","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, K.\u00a0Ramakrishnan College of Technology, Tiruchirappalli 621112, Tamil Nadu, India"}]}],"member":"219","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"S0218126622502267BIB001","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"S0218126622502267BIB002","author":"Kim M. 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