scholarly journals On-chip learning for domain wall synapse based Fully Connected Neural Network

2019 ◽  
Vol 489 ◽  
pp. 165434 ◽  
Author(s):  
Debanjan Bhowmik ◽  
Utkarsh Saxena ◽  
Apoorv Dankar ◽  
Anand Verma ◽  
Divya Kaushik ◽  
...  
Keyword(s):  
Neural Network ◽  
Domain Wall ◽  
On Chip ◽  
Fully Connected

scholarly journals Comparing domain wall synapse with other non volatile memory devices for on-chip learning in analog hardware neural network

AIP Advances ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 025111 ◽  
Author(s):  
Divya Kaushik ◽  
Utkarsh Singh ◽  
Upasana Sahu ◽  
Indu Sreedevi ◽  
Debanjan Bhowmik
Keyword(s):  
Neural Network ◽  
Domain Wall ◽  
Memory Devices ◽  
Non Volatile Memory ◽  
Volatile Memory ◽  
On Chip ◽  
Hardware Neural Network

scholarly journals Ferrimagnetic Synapse Devices for Fast and Energy-Efficient On-Chip Learning on An Analog-Hardware Neural Network

2021 ◽  
Author(s):  
Upasana Sahu ◽  
Naven Sisodia ◽  
Janak Sharda ◽  
Pranaba Kishor Muduli ◽  
Debanjan Bhowmik
Keyword(s):  
Domain Wall ◽  
Energy Efficient ◽  
Velocity Ratio ◽  
Domain Wall Motion ◽  
System Level ◽  
System Level Simulation ◽  
On Chip ◽  
Experimental Findings ◽  
Fully Connected ◽  
Hardware Neural Network

we have modeled domain-wall motion in ferrimagnetic and ferromagnetic devices through micro magnetics and shown that the domain-wall velocity can be 2–2.5X faster in the ferrimagnetic device compared to the ferromagnetic device. We also show that this velocity ratio is consistent with recent experimental findings Because of such a velocity ratio, when such devices are used as synapses in the crossbar-array-based fully connected network, our system-level simulation here shows that a ferrimagnet-synapse-based crossbar offers 4X faster (for the same energy efficiency) or 4X more energy-efficient (for the same speed) learning when compared to the ferromagnet-synapse-based crossbar.

scholarly journals Ferrimagnetic Synapse Devices for Fast and Energy-Efficient On-Chip Learning on An Analog-Hardware Neural Network

2021 ◽  
Author(s):  
Upasana Sahu ◽  
Naven Sisodia ◽  
Janak Sharda ◽  
Pranaba Kishor Muduli ◽  
Debanjan Bhowmik
Keyword(s):  
Domain Wall ◽  
Energy Efficient ◽  
Velocity Ratio ◽  
Domain Wall Motion ◽  
System Level ◽  
System Level Simulation ◽  
On Chip ◽  
Experimental Findings ◽  
Fully Connected ◽  
Hardware Neural Network

we have modeled domain-wall motion in ferrimagnetic and ferromagnetic devices through micro magnetics and shown that the domain-wall velocity can be 2–2.5X faster in the ferrimagnetic device compared to the ferromagnetic device. We also show that this velocity ratio is consistent with recent experimental findings Because of such a velocity ratio, when such devices are used as synapses in the crossbar-array-based fully connected network, our system-level simulation here shows that a ferrimagnet-synapse-based crossbar offers 4X faster (for the same energy efficiency) or 4X more energy-efficient (for the same speed) learning when compared to the ferromagnet-synapse-based crossbar.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

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 Radar Emitter Identification Based on Fully Connected Spiking Neural Network

2021 ◽  
Vol 1914 (1) ◽  
pp. 012036
Author(s):  
LI Wei ◽  
Zhu Wei-gang ◽  
Pang Hong-feng ◽  
Zhao Hong-yu
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Fully Connected

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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