{"id":11150,"date":"2026-02-01T04:14:25","date_gmt":"2026-02-01T04:14:25","guid":{"rendered":"https:\/\/mcsoc-forum.org\/site\/?page_id=11150"},"modified":"2026-02-10T07:29:56","modified_gmt":"2026-02-10T07:29:56","slug":"neurocore-t8","status":"publish","type":"page","link":"https:\/\/mcsoc-forum.org\/site\/index.php\/neurocore-t8\/","title":{"rendered":"Energy Efficient Neuromorphic Accelerators for Edge and Cloud"},"content":{"rendered":"\n
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\n \n \u2190 Back to the NeuroCore 2026 Page\n <\/a>\n<\/p>\n

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Track Chair<\/h2>\n
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\"Prof.<\/figure>

Farooq A. Khanday<\/a><\/strong>
University of Kashmir, India<\/span><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

\n Call for Papers \u2014 Energy Efficient Neuromorphic Accelerators for Edge and Cloud\n<\/h3>\n\n

\n The Energy Efficient Neuromorphic Accelerators for Edge and Cloud<\/strong> track invites original research\n contributions advancing low-power, high-performance neuromorphic computing across distributed environments.\n As AI workloads increasingly demand real-time processing, ultra-low-latency inference, and sustainable energy\n profiles, neuromorphic accelerators are emerging as a transformative solution for both edge devices and\n large-scale cloud infrastructures.\n<\/p>\n\n

\n This track welcomes innovations in architecture, circuits, algorithms, and system-level design that push the\n boundaries of energy efficiency, scalability, and deployment readiness. Topics of interest include, but are not\n limited to:\n<\/p>\n\n