output
20072023
most cited11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks

1.5k citations

9 papers

q-bio.PE2023★ 16 cited

Mathematical models of Plasmodium vivax transmission: a scoping review

Md Nurul Anwar, Lauren Smith, Angela Devine +8

Plasmodium vivax is one of the most geographically widespread malaria parasites in the world due to its ability to remain dormant in the human liver as hypnozoites and subsequently…

stat.ME2023

Quantifying the HIV reservoir with dilution assays and deep viral sequencing

Sarah C. Lotspeich, Brian D. Richardson, Pedro L. Baldoni +2

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these res…

eess.SP2021★ 4 cited

Photonic single perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks

Mengxi Tan, Xingyuan Xu, David J. Moss

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated K…

cs.ET2021★ 2 cited

Optical neuromorphic processing at Tera-OP/s speeds based on Kerr soliton crystal microcombs

Mengxi Tan, Xingyuan Xu, David J. Moss

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features…

physics.app-ph2021★ 66 cited

Soliton crystal Kerr microcombs for high-speed, scalable optical neural networks at 10 GigaOPs/s

Xingyuan Xu, Mengxi Tan, David J. Moss

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr…

cs.NE2020★ 1.5k cited

11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks

Xingyuan Xu, Mengxi Tan, Bill Corcoran +9

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features…