scholarly journals Scalability of All-Optical Neural Networks Based on Spatial Light Modulators

2021 ◽  
Vol 15 (5) ◽  
Author(s):  
Ying Zuo ◽  
Yujun Zhao ◽  
You-Chiuan Chen ◽  
Shengwang Du ◽  
Junwei Liu
Keyword(s):  
Neural Networks ◽  
Spatial Light Modulators ◽  
Light Modulators ◽  
Optical Neural Networks ◽  
All Optical

OPTICALLY ADDRESSED SPATIAL LIGHT MODULATORS FOR 3D DISPLAY

2011 ◽  
Vol 20 (04) ◽  
pp. 453-457 ◽  
Author(s):  
N. COLLINGS
Keyword(s):  
Neural Networks ◽  
Adaptive Optics ◽  
Spatial Light Modulator ◽  
Spatial Light Modulators ◽  
Current Interest ◽  
3D Display ◽  
Light Modulator ◽  
Optical Correlation ◽  
Light Modulators ◽  
Optical Neural Networks

An optically addressed spatial light modulator (OASLM) records the image on a write beam and transfers it to a read beam. Some example application areas are: image transduction; optical correlation; adaptive optics; and optical neural networks. Current interest in OASLMs has been generated by the work of Qinetiq on 3D display. This work is based on Active tiling, where an image can be recorded in one part of the device and is memorised, whilst the remainder of the device is updated with images. This paper will explain this system and survey the technological alternatives for this application.

Deformable mirror device spatial light modulators and their applicability to optical neural networks

1989 ◽  
Vol 28 (22) ◽  
pp. 4900 ◽  
Author(s):  
Dean R. Collins ◽  
Jeffrey B. Sampsell ◽  
Larry J. Hornbeck ◽  
James M. Florence ◽  
P. Andrew Penz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deformable Mirror ◽  
Spatial Light Modulators ◽  
Light Modulators ◽  
Optical Neural Networks

Smart VLSI/FELC spatial light modulators for neural networks

1994 ◽  
Author(s):  
T.C. B. Yu ◽  
Robert J. Mears ◽  
Anthony B. Davey ◽  
William A. Crossland ◽  
M. W. Snook ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spatial Light Modulators ◽  
Light Modulators

A New Optical Neuron Device for All-Optical Neural Networks

1991 ◽  
Author(s):  
Akio Takimoto ◽  
Koji Akiyama ◽  
Michihiro Miyauchi ◽  
Yasunori Kuratomi ◽  
Junko Asayama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Neural Networks ◽  
All Optical

TAG: A Neural Network Model for Large-Scale Optical Implementation

1991 ◽  
Vol 3 (1) ◽  
pp. 135-143 ◽  
Author(s):  
Hyuek-Jae Lee ◽  
Soo-Young Lee ◽  
Sang-Yung Shin ◽  
Bo-Yun Koh
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Single Layer ◽  
Spatial Light Modulators ◽  
Light Modulators ◽  
Different Types ◽  
Multilayer Architecture ◽  
Output Neurons

TAG (Training by Adaptive Gain) is a new adaptive learning algorithm developed for optical implementation of large-scale artificial neural networks. For fully interconnected single-layer neural networks with N input and M output neurons TAG contains two different types of interconnections, i.e., M N global fixed interconnections and N + M adaptive gain controls. For two-dimensional input patterns the former may be achieved by multifacet holograms, and the latter by spatial light modulators (SLMs). For the same number of input and output neurons TAG requires much less adaptive elements, and provides a possibility for large-scale optical implementation at some sacrifice in performance as compared to the perceptron. The training algorithm is based on gradient descent and error backpropagation, and is easily extensible to multilayer architecture. Computer simulation demonstrates reasonable performance of TAG compared to perceptron performance. An electrooptical implementation of TAG is also proposed.

scholarly journals All-Chalcogenide Programmable All-Optical Deep Neural Networks

2021 ◽  
Author(s):  
Ting Yu ◽  
Xiaoxuan Ma ◽  
Ernest Pastor ◽  
Jonathan George ◽  
Simon Wall ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Autonomous Vehicles ◽  
Structural Phase ◽  
Activation Function ◽  
Navigation Systems ◽  
Optical Neural Networks ◽  
Electro Optic ◽  
All Optical ◽  
On Chip

Abstract Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any electro-optic conversions could alleviate these shortcomings. However, an all-optical nonlinear activation function, which is a vital building block for optical neural networks, needs to be developed efficiently on-chip. Here, we introduce and demonstrate both optical synapse weighting and all-optical nonlinear thresholding using two different effects in one single chalcogenide material. We show how the structural phase transitions in a wide-bandgap phase-change material enables storing the neural network weights via non-volatile photonic memory, whilst resonant bond destabilisation is used as a nonlinear activation threshold without changing the material. These two different transitions within chalcogenides enable programmable neural networks with near-zero static power consumption once trained, in addition to picosecond delays performing inference tasks not limited by wire charging that limit electrical circuits; for instance, we show that nanosecond-order weight programming and near-instantaneous weight updates enable accurate inference tasks within 20 picoseconds in a 3-layer all-optical neural network. Optical neural networks that bypass electro-optic conversion altogether hold promise for network-edge machine learning applications where decision-making in real-time are critical, such as for autonomous vehicles or navigation systems such as signal pre-processing of LIDAR systems.

All-optical liquid crystal spatial light modulators

2003 ◽  
Author(s):  
Nelson Tabiryan ◽  
Vladimir Grozhik ◽  
Iam Choon Khoo ◽  
Sarik R. Nersisyan ◽  
Svetlana Serak
Keyword(s):  
Liquid Crystal ◽  
Spatial Light Modulators ◽  
Light Modulators ◽  
All Optical
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