{"id":301,"date":"2022-01-25T17:51:15","date_gmt":"2022-01-25T17:51:15","guid":{"rendered":"https:\/\/mcsoc-forum.org\/site\/?page_id=301"},"modified":"2026-01-29T07:52:17","modified_gmt":"2026-01-29T07:52:17","slug":"track-ml-efficiency","status":"publish","type":"page","link":"https:\/\/mcsoc-forum.org\/site\/index.php\/track-ml-efficiency\/","title":{"rendered":"Machine Learning for Energy-Efficient and Reliable Manycore Systems"},"content":{"rendered":"\n
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Track Chairs<\/h2>\n
\n
\"Prof.\n

Prof. Md. Farhadur Reza<\/a><\/strong>
Eastern Illinois University, USA<\/span><\/p>\n<\/div>\n

\"Prof.\n

Prof. Hanzhi Ma<\/a><\/strong>
Zhejiang University, China<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

Computing systems have been an essential factor in advancing technology and applications, and machine learning is no different. Large servers have enabled complex machine-learning algorithms to achieve high performance in cognitive applications. However, with the rising need for advanced machine learning for large-scale applications, scalable, low-cost, high-performance, reliable, and energy-efficient manycore system on chip (MCSoCs) have become crucial. The topics of interest of the track include, but are not limited to:\u00a0<\/p>\n\n\n\n