{"id":297,"date":"2022-01-25T17:49:52","date_gmt":"2022-01-25T17:49:52","guid":{"rendered":"https:\/\/mcsoc-forum.org\/site\/?page_id=297"},"modified":"2026-01-29T07:53:28","modified_gmt":"2026-01-29T07:53:28","slug":"track-chiplet","status":"publish","type":"page","link":"https:\/\/mcsoc-forum.org\/site\/index.php\/track-chiplet\/","title":{"rendered":"Chiplet-Based Architectures and Design for Multicore SoCs"},"content":{"rendered":"\n
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Track Chairs<\/h2>\n
\n
\"Jason\n

Jason Eshraghian<\/a><\/strong>
UC Santa Cruz, USA<\/span><\/p>\n<\/div>\n

\"Darshika\n

Darshika G. Perera<\/a><\/strong>
University of Colorado, USA<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

The semiconductor industry is undergoing a significant transformation, shifting from traditional monolithic processors to multi-core and many-core architectures. These advanced designs, which can be integrated onto a single chip or within emerging chiplet-based systems, leverage cutting-edge packaging technologies and standardized interfaces. This innovation offers greater flexibility, accelerated time-to-market, and enhanced cost-efficiency, marking a pivotal change in client devices, server processors, and a broad range of emerging applications.
As generative AI models grow increasingly complex, with Large Language Model (LLM) parameters reaching billions, the demand for computational power intensifies across the software-hardware ecosystem. Chiplet-based architectures present an exciting solution to address these challenges, providing scalable, energy-efficient, and cost-effective processing capabilities. This advancement has the potential to redefine the future of cognitive systems and unlock new opportunities for AI-driven applications.

The topics of interest of the track include, but are not limited to:<\/p>\n\n\n\n