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Online training and pruning of multi-wavelength photonic neural networks
Jiawei Zhang
, Weipeng Zhang
, Tengji Xu
, Lei Xu
, Eli A. Doris
, Bhavin J. Shastri
, Chaoran Huang
,
Paul R. Prucnal
Electrical and Computer Engineering
NextG
Princeton Materials Institute
Research output
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Contribution to journal
›
Article
›
peer-review
2
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Dive into the research topics of 'Online training and pruning of multi-wavelength photonic neural networks'. Together they form a unique fingerprint.
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Keyphrases
Online Training
100%
Microring Resonator
100%
Multi-wavelength Photonic Neural Networks
100%
Photonic Neural Networks
50%
Photonic Integrated Circuits
33%
Energy Efficiency
33%
Resonance Wavelength
33%
Fabrication Variation
33%
Neural Network
16%
Loss Function
16%
On chip
16%
Order of Magnitude
16%
Fabrication Methods
16%
Artificial Intelligence
16%
LiDAR
16%
Pruning Strategy
16%
Wide Temperature Range
16%
Power Consumption
16%
Beamforming
16%
CMOS Technology
16%
Wavelength Division multiplexing
16%
Process Complexity
16%
Power Requirement
16%
Thermally Induced
16%
Power-aware
16%
Photonic Switching
16%
Control Power
16%
Total Energy Consumption
16%
Switching Frequency
16%
Classification Accuracy
16%
Training Methods
16%
Environmental Fluctuations
16%
Analog Photonics
16%
Large-scale Integration
16%
Photonic Processors
16%
Network Accuracy
16%
Compact Footprint
16%
Versatile Applications
16%
Iris Dataset
16%
Radiofrequency
16%
Chip-based
16%
Minimizing Power
16%
Intelligence Computing
16%
Thermo-optic Tuning
16%
Physics
Resonator
100%
Photonic Neural Networks
100%
Photonic Integrated Circuits
33%
Energy Efficiency
33%
Photonics
16%
Optical Switching
16%
Optics
16%
Neural Network
16%
Beamforming
16%
Wavelength-Division Multiplexing
16%
Optical Radar
16%
Artificial Intelligence
16%
Computer Science
Neural Network
100%
Process Variation
40%
Energy Efficiency
40%
Power Consumption
40%
Integrated Circuit
40%
Classification Accuracy
20%
Wavelength Division Multiplexing
20%
Artificial Intelligence
20%
Proof
20%
Scale Integration
20%
Chip Variation
20%