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Found 5 papers, 1 papers with code
IR-Aware ECO Timing Optimization Using Reinforcement Learning
Engineering change orders (ECOs) in late stages make minimal design fixes to recover from timing shifts due to excessive IR drops.
A Machine Learning Approach to Improving Timing Consistency between Global Route and Detailed Route
Inaccurate timing prediction wastes design effort, hurts circuit performance, and may lead to design failure.
Encoder-Decoder Networks for Analyzing Thermal and Power Delivery Networks
For PDN analysis, we propose two networks: (i) IREDGe: a full-chip static and dynamic IR drop predictor and (ii) EMEDGe: electromigration (EM) hotspot classifier based on input power, power grid distribution, and power pad distribution patterns.
OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis
Power delivery network (PDN) design is a nontrivial, time-intensive, and iterative task.
Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks
Computationally expensive temperature and power grid analyses are required during the design cycle to guide IC design.
