Effective Post-Training Quantization Of Neural Networks For Inference on Low Power Neural Accelerator

2020 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn48605.2020.9207281 ◽

2020 ◽

Author(s):

Alexander Demidovskij ◽

Eugene Smirnov

Keyword(s):

Neural Networks ◽

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Neural Networks: Low‐Power Self‐Rectifying Memristive Artificial Neural Network for Near Internet‐of‐Things Sensor Computing (Adv. Electron. Mater. 6/2021)

Advanced Electronic Materials ◽

10.1002/aelm.202170017 ◽

2021 ◽

Vol 7 (6) ◽

pp. 2170017

Author(s):

Seok Choi ◽

Yong Kim ◽

Tien Van Nguyen ◽

Won Hee Jeong ◽

Kyeong‐Sik Min ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Internet Of Things ◽

Low Power ◽

Artificial Neural

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Convolutional Neural Networks-based plant disease detection implemented on low-power consumption device

10.1109/ropec53248.2021.9668160 ◽

2021 ◽

Author(s):

Eduardo A. Huerta Mora ◽

Victor Gonzalez-Huitron ◽

A.E. Rodriguez-Mata ◽

Hector Rodriguez Rangel

Keyword(s):

Neural Networks ◽

Power Consumption ◽

Low Power ◽

Convolutional Neural Networks ◽

Plant Disease ◽

Low Power Consumption ◽

Disease Detection

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Low power building block for artificial neural networks

Electronics Letters ◽

10.1049/el:19951138 ◽

1995 ◽

Vol 31 (19) ◽

pp. 1618-1619 ◽

Author(s):

S.T. Lee ◽

K.T. Lau

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Low Power ◽

Building Block ◽

Artificial Neural

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Arbitrary-Precision Convolutional Neural Networks on Low-Power IoT Processors

2019 IFIP/IEEE 27th International Conference on Very Large Scale Integration (VLSI-SoC) ◽

10.1109/vlsi-soc.2019.8920341 ◽

2019 ◽

Author(s):

Valentino Peluso ◽

Matteo Grimaldi ◽

Andrea Calimera

Keyword(s):

Neural Networks ◽

Low Power ◽

Convolutional Neural Networks ◽

Arbitrary Precision

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Predictive Maintenance of Induction Motors Using Ultra-Low Power Wireless Sensors and Compressed Recurrent Neural Networks

IEEE Access ◽

10.1109/access.2019.2953019 ◽

2019 ◽

Vol 7 ◽

pp. 178891-178902 ◽

Author(s):

Michal Markiewicz ◽

Maciej Wielgosz ◽

Mikolaj Bochenski ◽

Waldemar Tabaczynski ◽

Tomasz Konieczny ◽

...

Keyword(s):

Neural Networks ◽

Low Power ◽

Recurrent Neural Networks ◽

Wireless Sensors ◽

Induction Motors ◽

Predictive Maintenance ◽

Ultra Low Power

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Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse

2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) ◽

10.1109/aicas48895.2020.9073877 ◽

2020 ◽

Author(s):

A. Laborieux ◽

M. Bocquet ◽

T. Hirtzlin ◽

J.-O. Klein ◽

L. Herrera Diez ◽

...

Keyword(s):

Neural Networks ◽

Low Power ◽

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A Mathematical Approach Towards Quantization of Floating Point Weights in Low Power Neural Networks

2020 33rd International Conference on VLSI Design and 2020 19th International Conference on Embedded Systems (VLSID) ◽

10.1109/vlsid49098.2020.00048 ◽

2020 ◽

Author(s):

Joydeep Kumar Devnath ◽

Neelam Surana ◽

Joycee Mekie

Keyword(s):

Neural Networks ◽

Low Power ◽

Floating Point ◽

Mathematical Approach

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A Configurable and Versatile Architecture for Low Power, Energy Efficient Hardware Acceleration of Convolutional Neural Networks

2019 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC) ◽

10.1109/norchip.2019.8906950 ◽

2019 ◽

Author(s):

Steinar Thune Christensen ◽

Snorre Aunet ◽

Omer Qadir

Keyword(s):

Neural Networks ◽

Low Power ◽

Convolutional Neural Networks ◽

Energy Efficient ◽

Hardware Acceleration

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Optimal Design Methods to Transform 3D NAND Flash into a High-Density, High-Bandwidth and Low-Power Nonvolatile Computing in Memory (nvCIM) Accelerator for Deep-Learning Neural Networks (DNN)

2019 IEEE International Electron Devices Meeting (IEDM) ◽

10.1109/iedm19573.2019.8993652 ◽

2019 ◽

Author(s):

Hang-Ting Lue ◽

Po-Kai Hsu ◽

Ming-Liang Wei ◽

Teng-Hao Yeh ◽

Pei-Ying Du ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Optimal Design ◽

Low Power ◽

Design Methods ◽

High Density ◽

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Low-Power Binary Neuron Circuit With Adjustable Threshold for Binary Neural Networks Using NAND Flash Memory

IEEE Access ◽

10.1109/access.2020.3018226 ◽

2020 ◽

Vol 8 ◽

pp. 153334-153340

Author(s):

Sung-Tae Lee ◽

Sung Yun Woo ◽

Jong-Ho Lee

Keyword(s):

Neural Networks ◽

Low Power ◽

Flash Memory ◽

Nand Flash Memory ◽

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