28 citations · 39 across the 18 of their papers we have counts for
6 papers · 1 filter
GROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
Kiran Thorat, Hongwu Peng, Yuebo Luo +8
Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gain…
Differentiable Combinatorial Scheduling at Scale
Mingju Liu, Yingjie Li, Jiaqi Yin +2
This paper addresses the complex issue of resource-constrained scheduling, an NP-hard problem that spans critical areas including chip design and high-performance computing. Tradit…
Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs
Yingjie Li, Mingju Liu, Alan Mishchenko +1
The complexity of modern hardware designs necessitates advanced methodologies for optimizing and analyzing modern digital systems. In recent times, machine learning (ML) methodolog…
Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture
Yingjie Li, Weilu Gao, Cunxi Yu
Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, paral…
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
Shanglin Zhou, Yingjie Li, Minhan Lou +4
As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural…
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
Yingjie Li, Ruiyang Chen, Berardi Sensale Rodriguez +2
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing…