activity
20192022
most citedA Deep Reinforcement Learning Approach for Global Routing

9 citations · 23 across the 4 of their papers we have counts for

collaborators

5 papers

cs.NE20221 cited

Hierarchical Automatic Power Plane Generation with Genetic Optimization and Multilayer Perceptron

Haiguang Liao, Vinay Patil, Xuliang Dong +5

We present an automatic multilayer power plane generation method to accelerate the design of printed circuit boards (PCB). In PCB design, while automatic solvers have been develope…

cs.AI20204 cited

Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing

Dhruv Vashisht, Harshit Rampal, Haiguang Liao +4

Physical design and production of Integrated Circuits (IC) is becoming increasingly more challenging as the sophistication in IC technology is steadily increasing. Placement has be…

cs.LG2020

Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision

Haiguang Liao, Qingyi Dong, Weiyi Qi +2

Detailed routing is one of the most critical steps in analog circuit design. Complete routing has become increasingly more challenging in advanced node analog circuits, making adva…

cs.LG20209 cited

Attention Routing: track-assignment detailed routing using attention-based reinforcement learning

Haiguang Liao, Qingyi Dong, Xuliang Dong +5

In the physical design of integrated circuits, global and detailed routing are critical stages involving the determination of the interconnected paths of each net on a circuit whil…

cs.LG20199 cited

A Deep Reinforcement Learning Approach for Global Routing

Haiguang Liao, Wentai Zhang, Xuliang Dong +3

Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with w…