Presentation
GNN-Opt: Enhancing Automated Circuit Design Optimization with Graph Neural Networks
DescriptionWe focus on the high learning efficiency of critic networks in "DNN-Opt", an automated op-amp sizing algorithm using reinforcement learning, and propose "GNN-Opt", a replacement for GNN architecture that can work across different topologies. When sizing was performed on the NMOS basic differential pair, GNN-Opt not only learned criticism and sized to calculate high FoM as well as DNN-Opt, but also obtained high FoM from the beginning by converting the training model to a PMOS differential pair and performing inference only, and achieved high FoM without learning. The performance was higher than that of the model without learning the NMOS design.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security