Application of self-organized genetic algorithms to a novel color recognition system of all-optical neural networks

1999 ◽  
Author(s):  
Wakao Sasaki ◽  
Hiroyuki Uchida ◽  
Keishi Takahashi
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Recognition System ◽  
Color Recognition ◽  
Self Organized ◽  
Optical Neural Networks ◽  
All Optical

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

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.

Reversal-input superposing technique for all-optical neural networks

1994 ◽  
Vol 33 (8) ◽  
pp. 1477 ◽  
Author(s):  
Yoshio Hayasaki ◽  
Ichiro Tohyama ◽  
Toyohiko Yatagai ◽  
Masahiko Mori ◽  
Satoshi Ishihara
Keyword(s):  
Neural Networks ◽  
Optical Neural Networks ◽  
All Optical

A new optical neuron device for all-optical neural networks

2002 ◽  
Author(s):  
A. Takimoto ◽  
K. Akiyama ◽  
M. Miyauchi ◽  
Y. Kuratomi ◽  
J. Asayama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Neural Networks ◽  
All Optical

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

A New Optical Neuron Device for All-Optical Neural Networks

1991 ◽  
Vol 30 (Part 1, No. 12B) ◽  
pp. 3887-3892 ◽  
Author(s):  
Koji Akiyama ◽  
Akio Takimoto ◽  
Michihiro Miyauchi ◽  
Yasunori Kuratomi ◽  
Junko Asayama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Neural Networks ◽  
All Optical

scholarly journals A scheme for simulating multi-level phase change photonics materials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yunzheng Wang ◽  
Jing Ning ◽  
Li Lu ◽  
Michel Bosman ◽  
Robert E. Simpson
Keyword(s):  
Neural Networks ◽  
Phase Transitions ◽  
Phase Change ◽  
Phase Change Materials ◽  
Effective Medium Theory ◽  
Optical Response ◽  
Nanosecond Laser ◽  
Optical Neural Networks ◽  
All Optical ◽  
Multi Level

AbstractChalcogenide phase change materials (PCMs) have been extensively applied in data storage, and they are now being proposed for high resolution displays, holographic displays, reprogrammable photonics, and all-optical neural networks. These wide-ranging applications all exploit the radical property contrast between the PCMs’ different structural phases, extremely fast switching speed, long-term stability, and low energy consumption. Designing PCM photonic devices requires an accurate model to predict the response of the device during phase transitions. Here, we describe an approach that accurately predicts the microstructure and optical response of phase change materials during laser induced heating. The framework couples the Gillespie Cellular Automata approach for modelling phase transitions with effective medium theory and Fresnel equations. The accuracy of the approach is verified by comparing the PCM’s optical response and microstructure evolution with the results of nanosecond laser switching experiments. We anticipate that this approach to simulating the switching response of PCMs will become an important component for designing and simulating programmable photonics devices. The method is particularly important for predicting the multi-level optical response of PCMs, which is important for all-optical neural networks and PCM-programmable perceptrons.

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