scholarly journals On-chip photonic diffractive optical neural network based on spatial domain electromagnetic propagation model

2021 ◽  
Author(s):  
Tingzhao Fu ◽  
Yubin Zang ◽  
Honghao Huang ◽  
Zhenmin Du ◽  
Chengyang Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Spatial Domain ◽  
Propagation Model ◽  
Electromagnetic Propagation ◽  
Optical Neural Network ◽  
On Chip

scholarly journals On-chip Reconfigurable Optical Neural Networks

2021 ◽  
Author(s):  
Xianmeng Zhao ◽  
Haibin Lv ◽  
Cheng Chen ◽  
Shenjie Tang ◽  
Xiaoping Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optical Networks ◽  
Full Adder ◽  
Silicon On Insulator ◽  
Neuron Networks ◽  
Control Electronics ◽  
Optical Neural Network ◽  
Vowel Recognition ◽  
On Chip

Abstract Implementing artificial neural networks on integrated platforms has generated significant interest in recent years. Several architectures for on-chip optical networks with basic functionalities have been successfully demonstrated, for example, optical spiking neurosynaptic, photonic convolution accelerator, and nanophotonic/electronic hybrid deep neuron networks. In this work, we propose a layered coherent silicon-on-insulator diffractive optical neural network, of which the inter-layer phase delay can be actively tuned. By forming a close-loop with control electronics, we further demonstrate that our fabricated on-chip neural network can be trained in-situ and consequently reconfigured to perform various tasks, including full adder operation and vowel recognition, while achieving almost the same accuracy as networks trained on conventional computers. Our results show that the proposed optical neural network could potentially pave the way for future optical artificial intelligence hardware.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Ray Tracing Modeling of Electromagnetic Propagation for On-Chip Wireless Optical Communications

2018 ◽  
Vol 8 (4) ◽  
pp. 39 ◽  
Author(s):  
Franco Fuschini ◽  
Marina Barbiroli ◽  
Marco Zoli ◽  
Gaetano Bellanca ◽  
Giovanna Calò ◽  
...  
Keyword(s):  
Ray Tracing ◽  
Path Loss ◽  
Wireless Channel ◽  
Electromagnetic Propagation ◽  
Networks On Chip ◽  
Spatial Properties ◽  
Radiation Properties ◽  
Wireless Optical Communications ◽  
On Chip ◽  
The Relationship

Multi-core processors are likely to be a point of no return to meet the unending demand for increasing computational power. Nevertheless, the physical interconnection of many cores might currently represent the bottleneck toward kilo-core architectures. Optical wireless networks on-chip are therefore being considered as promising solutions to overcome the technological limits of wired interconnects. In this work, the spatial properties of the on-chip wireless channel are investigated through a ray tracing approach applied to a layered representation of the chip structure, highlighting the relationship between path loss, antenna positions and radiation properties.

Implementation of an On-Chip Learning Neural Network IC Using Highly Linear Charge Trap Device

2021 ◽  
pp. 1-13
Author(s):  
Jong-Moon Choi ◽  
Do-Wan Kwon ◽  
Je-Joong Woo ◽  
Eun-Je Park ◽  
Kee-Won Kwon
Keyword(s):  
Neural Network ◽  
Linear Charge ◽  
Charge Trap ◽  
On Chip

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.

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