Presentation
ML Based Design Space Prediction for Power Grid Optimization
Description• Increasing design complexities, tighter noise margins, along with higher PPA (Power, Performance, Area) expectations with shorter design cycles in advanced FinFet technology nodes demands the need for a faster and exhaustively optimal power delivery network (PDN) synthesis. These PDNs have to optimally balance the EMIR budget needs as well as other design targets like PPA and routability.
• Traditional PDN synthesis methodologies employ the random iterative hit-and-trial approach by sweeping the very limited random set of design parameters, Hence they lack in coverage, sub-optimal for PPA and consumes long implementation cycles, which impacts the confidence on PDN quality and time-to-market.
• To overcome these limitations, We propose a faster and exhaustive ML based design space exploration methodology for optimal PDN synthesis that figures out the power integrity sensitive design parameters like critical power grid metal layers, metal width, metal pitch, bump locations etc. and the optimal value combinations of which meets the specified design targets. Our methodology offers better design coverage as designers can explore many more design parameters to sweep them across a wide range with fine grained steps along with faster run times to build and explore the analysis based meta models. Our results show a very well correlated meta model w.r.t simulations and shows better combinations of some of the specified metal layers (M6,M8,M9) widths and pitches for an optimal PDN which meets specified dynamic IR drop budgets with high confidence.
• Traditional PDN synthesis methodologies employ the random iterative hit-and-trial approach by sweeping the very limited random set of design parameters, Hence they lack in coverage, sub-optimal for PPA and consumes long implementation cycles, which impacts the confidence on PDN quality and time-to-market.
• To overcome these limitations, We propose a faster and exhaustive ML based design space exploration methodology for optimal PDN synthesis that figures out the power integrity sensitive design parameters like critical power grid metal layers, metal width, metal pitch, bump locations etc. and the optimal value combinations of which meets the specified design targets. Our methodology offers better design coverage as designers can explore many more design parameters to sweep them across a wide range with fine grained steps along with faster run times to build and explore the analysis based meta models. Our results show a very well correlated meta model w.r.t simulations and shows better combinations of some of the specified metal layers (M6,M8,M9) widths and pitches for an optimal PDN which meets specified dynamic IR drop budgets with high confidence.
Event Type
Engineering Poster
TimeWednesday, July 293:00pm - 4:00pm PDT
LocationDAC Pavilion, Exhibit Floor
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