Machine learning: Difference between revisions

Revision as of 18:53, 20 May 2025

Revision as of 02:02, 24 May 2025

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| pages = 14192–14205

| doi = 10.1109/JIOT.2023.3340858

| url = https://research-portal.uws.ac.uk/en/publications/c8edfe21-77d0-4c3e-a8bc-d384faf605a0

}}</ref> Running models directly on these devices eliminates the need to transfer and store data on cloud servers for further processing, thereby reducing the risk of data breaches, privacy leaks and theft of intellectual property, personal data and business secrets. Embedded machine learning can be achieved through various techniques, such as [[hardware acceleration]],<ref>{{Cite book|last1=Giri|first1=Davide|last2=Chiu|first2=Kuan-Lin|last3=Di Guglielmo|first3=Giuseppe|last4=Mantovani|first4=Paolo|last5=Carloni|first5=Luca P.|title=2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) |chapter=ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning |date=15 June 2020|chapter-url=https://ieeexplore.ieee.org/document/9116317|pages=1049–1054|doi=10.23919/DATE48585.2020.9116317|arxiv=2004.03640|isbn=978-3-9819263-4-7|s2cid=210928161|access-date=17 January 2022|archive-date=18 January 2022|archive-url=https://web.archive.org/web/20220118182342/https://ieeexplore.ieee.org/abstract/document/9116317?casa_token=5I_Tmgrrbu4AAAAA:v7pDHPEWlRuo2Vk3pU06194PO0-W21UOdyZqADrZxrRdPBZDMLwQrjJSAHUhHtzJmLu_VdgW|url-status=live}}</ref><ref>{{Cite web|last1=Louis|first1=Marcia Sahaya|last2=Azad|first2=Zahra|last3=Delshadtehrani|first3=Leila|last4=Gupta|first4=Suyog|last5=Warden|first5=Pete|last6=Reddi|first6=Vijay Janapa|last7=Joshi|first7=Ajay|date=2019|title=Towards Deep Learning using TensorFlow Lite on RISC-V|url=https://edge.seas.harvard.edu/publications/towards-deep-learning-using-tensorflow-lite-risc-v|access-date=17 January 2022|website=[[Harvard University]]|archive-date=17 January 2022|archive-url=https://web.archive.org/web/20220117031909/https://edge.seas.harvard.edu/publications/towards-deep-learning-using-tensorflow-lite-risc-v|url-status=live}}</ref> [[approximate computing]],<ref>{{Cite book|last1=Ibrahim|first1=Ali|last2=Osta|first2=Mario|last3=Alameh|first3=Mohamad|last4=Saleh|first4=Moustafa|last5=Chible|first5=Hussein|last6=Valle|first6=Maurizio|title=2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS) |chapter=Approximate Computing Methods for Embedded Machine Learning |date=21 January 2019|chapter-url=https://ieeexplore.ieee.org/document/8617877|pages=845–848|doi=10.1109/ICECS.2018.8617877|isbn=978-1-5386-9562-3|s2cid=58670712|access-date=17 January 2022|archive-date=17 January 2022|archive-url=https://web.archive.org/web/20220117031855/https://ieeexplore.ieee.org/abstract/document/8617877?casa_token=arUW5Oy-tzwAAAAA:I9x6edlfskM6kGNFUN9zAFrjEBv_8kYTz7ERTxtXu9jAqdrYCcDbbwjBdgwXvb6QAH_-0VJJ|url-status=live}}</ref> and model optimisation.<ref>{{Cite web|title=dblp: TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning.|url=https://dblp.org/rec/journals/corr/abs-1903-01855.html|access-date=17 January 2022|website=dblp.org|language=en|archive-date=18 January 2022|archive-url=https://web.archive.org/web/20220118182335/https://dblp.org/rec/journals/corr/abs-1903-01855.html|url-status=live}}</ref><ref>{{Cite journal|last1=Branco|first1=Sérgio|last2=Ferreira|first2=André G.|last3=Cabral|first3=Jorge|date=5 November 2019|title=Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey|journal=Electronics|volume=8|issue=11|pages=1289|doi=10.3390/electronics8111289|issn=2079-9292|doi-access=free|hdl=1822/62521|hdl-access=free}}</ref> Common optimisation techniques include [[Pruning (artificial neural network)|pruning]], [[Quantization (Embedded Machine Learning)|quantisation]], [[knowledge distillation]], low-rank factorisation, network architecture search, and parameter sharing.