Presentation
Advanced Analog Design Optimization: Comparison Between Reinforcement Learning and Heuristic Algorithms
DescriptionLast decades have seen a lot of research on Analog Design Automation. The most recent approaches are based on Reinforcement Learning (RL), instead of heuristic optimizers, such as ant colony, particle swarm or differential evolution algorithm. This paper describes a new learning strategy enhancing the most recent Proximal Policy Optimization (PPO) RL approach, applied to analog design. This solution is compared to more classical heuristic methods mentioned above. This study is done using an electrical-simulator-based environment under equivalent calculation conditions. The paper highlights convergence properties and demonstrates the RL ability to avoid local minimum traps.
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