Year: 2024
Type: dissertation
Abstract: In the field of Electronic Design Automation (EDA), dividing a complex circuit into smaller subcircuits is a critical step. This process, known as circuit partitioning, minimizes the connections needed between these subcircuits while dividing the overall design. This can simplify the problem, and further enhance the performance and efficiency of other EDA tasks, such as placement and routing. A popular method for circuit partitioning is the Fiduccia-Mattheyses-Sanchis (FMS) algorithm, which is a local search heuristic. While it is efficient, the FMS algorithm has two weaknesses: it relies heavily on a good initial solution and can become caught in suboptimal solutions. This research explores various methods to construct better initial solutions for circuit partitioning. As well, it investigates the application of reinforcement learning (RL) techniques to overcome the local minima problem inherent in the FMS algorithm, thereby achieving higher-quality partitions.
Author: Cheng, Ka Chuen
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204611933