8 citations · 17 across the 8 of their papers we have counts for
8 papers · 1 filter
AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction
Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang +3
As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacita…
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction
Zhixiong Di, Runzhe Tao, Lin Chen +2
Packing is a required step in a typical FPGA CAD flow. It has high impacts to the performance of FPGA placement and routing. Early prediction of packing results can guide design op…
HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction
Yuxiang Zhao, Zhuomin Chai, Yibo Lin +2
Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycl…
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Bowen Wang, Guibao Shen, Dong Li +7
Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation f…
Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview
Junchi Yan, Xianglong Lyu, Ruoyu Cheng +1
Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Compared with traditional solvers using heuristics or expert-well-designed…
Towards a Theoretical Understanding of Hashing-Based Neural Nets
Yibo Lin, Zhao Song, Lin F. Yang
Parameter reduction has been an important topic in deep learning due to the ever-increasing size of deep neural network models and the need to train and run them on resource limite…