Presentation
Multi-Terminal Pathfinding with Conditional Denoising Diffusion Probabilistic Model
DescriptionA conditional diffusion probabilistic neural network tailored for swift, scalable multiterminal obstacle-avoiding pathfinding within VLSI systems is introduced. This method departs from conventional pathfinding strategies by leveraging the unique capabilities of diffusion models, which translate pathfinding into a graphical representation for enhanced path generation. Based on experimental results, the runtime for this diffusion-based pathfinding method remains constant as system complexity increases, resulting in a wirelength similar to that of state-of-the-art. The constant runtime complexity along with lack of scalability limitations represent a significant improvement over traditional learning-based pathfinding methods, highlighting the potential of diffusion models to transform additional EDA applications.
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