{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:58:06Z","timestamp":1774025886212,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>The human brain performs tasks with an outstanding energy efficiency, i.e., with approximately 20 Watts. The state-of-the-art Artificial\/Deep Neural Networks (ANN\/DNN), on the other hand, have recently been shown to consume massive amounts of energy. The training of these ANNs\/DNNs is done almost exclusively based on the back-propagation algorithm, which is known to be biologically implausible. This has led to a new generation of forward-only techniques, including the Forward-Forward algorithm. In this paper, we propose a lightweight inference scheme specifically designed for DNNs trained using the Forward-Forward algorithm. We have evaluated our proposed lightweight inference scheme in the case of the MNIST and CIFAR datasets, as well as two real-world applications, namely, epileptic seizure detection and cardiac arrhythmia classification using wearable technologies, where complexity overheads\/energy consumption is a major constraint, and demonstrate its relevance. Our code is available at https:\/\/github.com\/AminAminifar\/LightFF.<\/jats:p>","DOI":"10.3233\/faia240682","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:06:02Z","timestamp":1729170362000},"source":"Crossref","is-referenced-by-count":5,"title":["LightFF: Lightweight Inference for Forward-Forward Algorithm"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9920-2539","authenticated-orcid":false,"given":"Amin","family":"Aminifar","sequence":"first","affiliation":[{"name":"Heidelberg University, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4010-8545","authenticated-orcid":false,"given":"Baichuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Lund University, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3642-8466","authenticated-orcid":false,"given":"Azra","family":"Abtahi","sequence":"additional","affiliation":[{"name":"Lund University, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1673-4733","authenticated-orcid":false,"given":"Amir","family":"Aminifar","sequence":"additional","affiliation":[{"name":"Lund University, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240682","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:06:02Z","timestamp":1729170362000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240682","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}