Presentation
Efficient Prediction of SRAM Read Access Time and Yield via Neural Network Leveraging Transfer Learning and Transformer Models
DescriptionWe propose neural network models that predict read access time (RAT) and read access yield (RAY) in SRAM, considering wide range of design variables. Using transfer learning, the RAT model reduces post-layout simulation time and training costs, achieving 1.2 million times faster prediction time of 0.18ms than HSPICE, with 2.14% error rate. The RAY model leverages transformer architecture for enhancing accuracy with 11k times faster prediction time of 0.27s than HSPICE, with 1.31% error rate. Both models save time for entire design process and enhance accuracy, with considering macro-level interactions and employing regularization methods specifically designed to effectively capture nonlinearities.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security