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Complexity brought on by advanced process nodes have opened the door to challenges in achieving optimal power, performance, and area (PPA). Manual methods are no longer viable given shrinking market windows. The need to drive for better results faster is increasing, and traditional methods cannot keep pace often taking months of tuning using 100s of trials. Even then, results are not optimal. AI-driven technology can help.

This ÁùºÏ²ÊÖ±²¥¿ª½± webinar discusses the challenges facing engineers creating complex designs and introduces ÁùºÏ²ÊÖ±²¥¿ª½± Design Space Optimization solution, DSO.ai?. The industry¡¯s first autonomous artificial intelligence (AI) application for chip design, ÁùºÏ²ÊÖ±²¥¿ª½± DSO.ai searches for optimization targets in very large solution spaces of chip design, utilizing reinforcement learning to enhance power, performance, and area. RTL-to-GDSII full flow optimization unlocks PPA potential across both logical and physical domains with reported productivity enhancements of more than 3x, power reductions of up to 15%, and substantial die size reductions. We¡¯ll explore case studies from AMD, Intel, and STMicro on how they are applying this technology to surpass the most challenging goals in chip design and reach new levels of productivity.