2 citations · 5 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks
Yingjie Li, Ruiyang Chen, Minhan Lou +3
To lower the barrier to diffractive optical neural networks (DONNs) design, exploration, and deployment, we propose LightRidge, the first end-to-end optical ML compilation framewor…
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…
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
Jiaqi Yin, Yingjie Li, Daniel Robinson +1
Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resourc…
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…