A Novel FPGA-Based Circuit Simulator for Accelerating Reinforcement Learning-Based Design of Power Converters
Document Type
Conference Proceeding
Date of Original Version
1-1-2023
Abstract
High-efficiency energy conversion systems have become increasingly important due to their wide use in all electronic systems such as data centers, smart mobile devices, E-vehicles, medical instruments, and so forth. Complex and interdependent parameters make optimal designs of power converters challenging to get. Recent research has shown that machine learning (ML) algorithms, such as reinforcement learning (RL), show great promise in design of such converter circuits. A trained RL agent can search for optimal design parameters for power conversion circuit topologies under targeted application requirements. Training an RL agent requires numerous circuit simulations. It requires significantly more training iterations when the tolerance of circuit components due to manufacturing inconsistency, aging, and temperature variation is considered. As a result, they may take days to complete, primarily because of the slow time-domain circuit simulation. This paper proposes a new FPGA architecture that accelerates the circuit simulation and hence substantially speeds up the RL-based design method for power converters. Our new architecture supports all power electronic circuit converters and their variations. It substantially improves the training speed of RL-based design methods. High-level synthesis (HLS) was used to build the accelerator on Amazon Web Service (AWS) F1 instance. An AWS virtual PC hosts the training algorithm. The host interacts with the FPGA accelerator by updating the circuit parameters, initiating simulation, and collecting the simulation results during training iterations. A script was created on the host side to facilitate this design method to convert a netlist containing circuit topology and parameters into core matrices in the FPGA accelerator. Experimental results showed 60 × overall speedup of our RL-based design method in comparison with using a popular commercial simulator, PowerSim.
Publication Title, e.g., Journal
Proceedings of the International Conference on Application Specific Systems Architectures and Processors
Volume
2023-July
Citation/Publisher Attribution
Xu, Zhenyu, Miaoxiang Yu, Jillian Cai, Qing Yang, Yeonho Jeong, and Tao Wei. "A Novel FPGA-Based Circuit Simulator for Accelerating Reinforcement Learning-Based Design of Power Converters." Proceedings of the International Conference on Application Specific Systems Architectures and Processors 2023-July, (2023). doi: 10.1109/ASAP57973.2023.00013.