Vaclav Snasel, Tran Khanh Dang, Phuong N. H. Pham, Josef Küng, Lingping Kong,
"In-Memory Computing Architectures for Big Data and Machine Learning Applications"
, in Tran Khanh Dang and Josef Küng and Tai M. Chung: Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23?25, 2022, Proceedings, Serie Communications in Computer and Information Science, Vol. 1688, Springer, Seite(n) 19-33, 2022
Original Titel:
In-Memory Computing Architectures for Big Data and Machine Learning Applications
Sprache des Titels:
Englisch
Original Buchtitel:
Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23?25, 2022, Proceedings
Original Kurzfassung:
Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research?s challenges and limitations and present possible directions.
Sprache der Kurzfassung:
Englisch
Veröffentlicher:
Springer
Serie:
Communications in Computer and Information Science
Forschungs-