Presentation
MACO: Model-based Adaptive Circuit Optimization by Transformer-based Bidirectional Predictions between Circuit Parameters and Specifications
DescriptionEfficient analog circuit design for given specifications is essential in the semiconductor industry, but it is challenging. To support this design process, various automation techniques have been proposed, but these barely utilize information gained from previous simulation data. As a result, learning-based methods utilizing neural networks have received much attention, since they have the ability to learn generalizability and a single neural network model can learn various tasks simultaneously. Inspired by this, we propose MACO, a transformer-based unified network for model-based optimization, designed for effective bidirectional prediction between circuit parameters and specifications across various circuit types. This framework is capable of handling diverse input lengths and providing variable-scale predictions, enhancing the optimization process, and helping circuit designers gain insight. We validate that MACO's learning efficiency is remarkably improved (more than 12X) compared to a single task learning.
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