Domain Knowledge-Infused Deep Learning for Automated Analog/RF Circuit Parameter Optimization*
TimeThursday, July 14th2:15pm - 2:37pm PDT
Location3007, Level 3
Event Type
Research Manuscript
AI/ML Design: Circuits and Architecture
Digital and Analog Circuits
DescriptionThis paper presents a deep reinforcement learning method to expedite the design of analog circuits at the pre-layout stage: finding device parameters to fulfill circuit specifications.
Our approach is inspired by experienced human designers who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem.
Unlike all prior methods, our method originally incorporates such key domain knowledge into policy learning, thereby best modeling the relations between circuit parameters and design targets.
Experimental results on exemplary circuits show it achieves human-level design accuracy (99%) with 1.5x efficiency of existing best-performing methods.