7 papers
All-Optical Segmentation via Diffractive Neural Networks for Autonomous Driving
Yingjie Li, Daniel Robinson, Weilu Gao +1
Semantic segmentation and lane detection are crucial tasks in autonomous driving systems. Conventional approaches predominantly rely on deep neural networks (DNNs), which incur hig…
HoloGraph: All-Optical Graph Learning via Light Diffraction
Yingjie Li, Shanglin Zhou, Caiwen Ding +1
As a representative of next-generation device/circuit technology beyond CMOS, physics-based neural networks such as Diffractive Optical Neural Networks (DONNs) have demonstrated pr…
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 Quadratic Optimization For The Maximum Independent Set Problem
Ismail Alkhouri, Cedric Le Denmat, Yingjie Li +4
Combinatorial Optimization (CO) addresses many important problems, including the challenging Maximum Independent Set (MIS) problem. Alongside exact and heuristic solvers, different…
MapTune: Advancing ASIC Technology Mapping via Reinforcement Learning Guided Library Tuning
Mingju Liu, Daniel Robinson, Yingjie Li +1
Technology mapping involves mapping logical circuits to a library of cells. Traditionally, the full technology library is used, leading to a large search space and potential overhe…
DAG-aware Synthesis Orchestration
Yingjie Li, Mingju Liu, Mark Ren +2
The key methodologies of modern logic synthesis techniques are conducted on multi-level technology-independent representations such as And-Inverter-Graphs (AIGs) of the digital log…