Presentation
Exploring Distributed Circuit Design Using Single-Step Reinforcement Learning
DescriptionThe inverse design in distributed circuits refers to generating circuits that tightly meet desirable specifications. Previous methods assume either over-restricted candidate templates or the differentiablity of the evaluation procedure. These assumptions are contrary to the real design practice which uses none-restrictive template types and non-differentiable evaluation procedures. In this paper, we propose Distributed Circuit Design Agent (DCDA), which generates distributed circuits to meet desirable transfer functions without any assumption regarding the design template types. Our agent trains a neural network that produces a near-optimal joint distribution as a set of conditional distributions to sample all design dimensions in a single step. We map sampled design dimensions to physical properties of resonators in order to establish physical evaluation feedback, which helps the agent adjust its sampling policy. Our experimental results show that without any assumption regarding template types, most generated distributed circuits from our method achieve better performance than those generated by the state-of-the-art approach in the inverse 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