63 citations · 352 across the 26 of their papers we have counts for
6 papers · 1 filter
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…
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…
Transition from Diffusive to Superdiffusive Transport in Carbon Nanotube Networks via Nematic Order Control
Michael Wais, Filchito Renee G. Bagsican, Natsumi Komatsu +8
The one-dimensional confinement of quasiparticles in individual carbon nanotubes (CNTs) leads to extremely anisotropic electronic and optical properties. In a macroscopic ensemble…
Scientific Computing with Diffractive Optical Neural Networks
Ruiyang Chen, Yingheng Tang, Jianzhu Ma +1
Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine v…
Controlled synthetic chirality in macroscopic assemblies of carbon nanotubes
Jacques Doumani, Minhan Lou, Oliver Dewey +9
There is an emerging recognition that successful utilization of chiral degrees of freedom can bring new scientific and technological opportunities to diverse research areas. Hence,…