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<p style="margin:0in"><span class="contentpasted0"><b><span style="font-family:"Calibri Light",sans-serif;color:#0D0D0D">Title:</span></b></span><span class="contentpasted0"><b><span style="font-family:Roboto;color:#0D0D0D">
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<span class="contentpasted0"><b><span style="font-family:Roboto;color:black">AI-Driven EDA: Transforming Chip Design for Performance, Cost Efficiency, and Reliability</span></b></span><b><span style="color:black">
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<p style="margin:0in"><span class="contentpasted0"><b><span style="font-family:Roboto;color:#0D0D0D">Bio:
</span></b></span><span class="contentpasted0"><span style="font-family:Roboto;color:#0D0D0D">Behnam Ghavami earned his Ph.D. in Computer Engineering from Amirkabir University of Technology in 2012. Following his doctoral studies, he served as an Assistant/Associate
Professor at the Department of Computer Engineering at Shahid Bahonar University of Kerman in Iran from 2013 to 2019. After his academic tenure, Behnam pursued a Postdoctoral Researcher position at the School of Engineering Science, Simon Fraser University,
in British Columbia, Canada. Presently, he holds the position of a postdoctoral researcher in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), concurrently serving as a Senior Member of Technical Staff at AMD.</span></span><span style="color:black"> <o:p></o:p></span></p>
<p style="margin:0in"><span class="contentpasted0"><span style="font-family:Roboto;color:#0D0D0D">Behnam's research expertise lies in electronic design automation (EDA), with a particular emphasis on the dependability and reliability of computer systems, as
well as adaptive computing systems. He is deeply committed to harnessing Machine Learning (ML) techniques within EDA to enhance the efficiency of intricate tasks while emphasizing the reinforcement of hardware reliability and security within VLSI systems.</span></span><span style="color:black"> <o:p></o:p></span></p>
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<p style="margin:0in"><span class="contentpasted0"><span style="font-family:"Calibri Light",sans-serif;color:#0D0D0D">Abstract</span></span><span class="contentpasted0"><i><span style="font-family:Roboto;color:#0D0D0D">-</span></i></span><span class="contentpasted0"><span style="font-family:Roboto;color:#0D0D0D">
The integration of artificial intelligence (AI) into electronic design automation (EDA) heralds a transformative era for chip design. This presentation explores the symbiotic relationship between AI-driven EDA and the evolution of chip design methodologies.
Leveraging generative AI and classical deep learning models, EDA processes are poised to optimize chip performance, efficiency, and societal impact while bolstering reliability and security. AI-driven EDA accelerates time to market, reduces chip design costs,
and democratizes access to advanced semiconductor technologies, fostering innovation for a more inclusive and prosperous society. Furthermore, Generative AI techniques pave the way for robust and secure chip architectures. The incorporation of AI in EDA significantly
impacts cost reduction, making chip design more accessible and affordable for diverse communities and industries. This talk elucidates how AI-driven EDA is reshaping the landscape of chip design, enabling high-performance, cost-efficient, and reliable solutions
that cater to the evolving needs of the semiconductor industry.</span></span><span style="color:black"> <o:p></o:p></span></p>
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