An optical neural network used for content addressable two-dimensional associative memory

2017 ◽  
Author(s):  
Yanping Sun
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Two Dimensional ◽  
Optical Neural Network

Continuous-time optical neural network associative memory

1989 ◽  
Vol 14 (3) ◽  
pp. 162 ◽  
Author(s):  
L.-S. Lee ◽  
H. M. Stoll ◽  
M. C. Tackitt
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Continuous Time ◽  
Optical Neural Network

Two-dimensional programmable optical neural network

1989 ◽  
Vol 28 (22) ◽  
pp. 4908 ◽  
Author(s):  
Taiwei Lu ◽  
Shudong Wu ◽  
Xin Xu ◽  
Francis T. S. Yu
Keyword(s):  
Neural Network ◽  
Two Dimensional ◽  
Optical Neural Network

scholarly journals Freely scalable and reconfigurable optical hardware for deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liane Bernstein ◽  
Alexander Sludds ◽  
Ryan Hamerly ◽  
Vivienne Sze ◽  
Joel Emer ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Data Transfer ◽  
Power Delivery ◽  
Optical Data ◽  
Optical Multicast ◽  
Optical Neural Network ◽  
Fully Connected ◽  
Management Power

AbstractAs deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.

scholarly journals Heat induction in two-dimensional graphene–Fe3O4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling

RSC Advances ◽  
2021 ◽  
Vol 11 (35) ◽  
pp. 21702-21715
Author(s):  
M. S. Dar ◽  
Khush Bakhat Akram ◽  
Ayesha Sohail ◽  
Fatima Arif ◽  
Fatemeh Zabihi ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Magnetic Hyperthermia ◽  
Alternating Magnetic Field ◽  
Neural Network Modeling ◽  
Two Dimensional ◽  
Artificial Neural Network Modeling ◽  
Heat Induction

Synthesis of Fe3O4–graphene (FG) nanohybrids and magnetothermal measurements of FxG100–x (x = 0, 25, 45, 65, 75, 85, 100) nanohybrids (25 mg each) at a 633 kHz alternating magnetic field of strength 9.1 mT.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

Convolutional Neural Network Based Symbol Detector for Two-dimensional Magnetic Recording

2020 ◽  
pp. 1-1
Author(s):  
Jinlu Shen ◽  
Benjamin J. Belzer ◽  
Krishnamoorthy Sivakumar ◽  
Kheong Sann Chan ◽  
Ashish James
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Magnetic Recording ◽  
Two Dimensional ◽  
Two Dimensional Magnetic Recording
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