A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

2019 ◽  
Author(s):  
Yuexuan Tu ◽  
Saad Sadiq ◽  
Yudong Tao ◽  
Mei-Ling Shyu ◽  
Shu-Ching Chen
Keyword(s):  
Neural Network ◽  
Power Efficient ◽  
Heterogeneous Fpga

scholarly journals Realization of a Power-Efficient Transmitter Based on Integrated Artificial Neural Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68773-68781 ◽  
Author(s):  
Deyu Kong ◽  
Shaogang Hu ◽  
Yuancong Wu ◽  
Junjie Wang ◽  
Canlong Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Efficient ◽  
Artificial Neural

Power-efficient Neural Network Scheduling

2022 ◽  
pp. 127-172
Author(s):  
Ying Wang ◽  
Xuyi Cai ◽  
Xiandong Zhao
Keyword(s):  
Neural Network ◽  
Network Scheduling ◽  
Power Efficient

Power-efficient neural network with artificial dendrites

2020 ◽  
Vol 15 (9) ◽  
pp. 776-782 ◽  
Author(s):  
Xinyi Li ◽  
Jianshi Tang ◽  
Qingtian Zhang ◽  
Bin Gao ◽  
J. Joshua Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Efficient

Power Efficient Multilayer Neural Network for Image Compression

2007 ◽  
Vol 6 (8) ◽  
pp. 1252-1257 ◽  
Author(s):  
K. Venkata Ramanaiah ◽  
K. Lal Kishore ◽  
P. Gopal Reddy
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Power Efficient

ΔNN: Power-efficient Neural Network Acceleration using Differential Weights

IEEE Micro ◽  
2019 ◽  
pp. 1-1
Author(s):  
Hoda Mahdiani ◽  
Alireza Khadem ◽  
Azam Ghanbari ◽  
Mehdi Modarressi ◽  
Farima Fattahi ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Efficient

scholarly journals Analog memristive synapse based on topotactic phase transition for high-performance neuromorphic computing and neural network pruning

2021 ◽  
Vol 7 (29) ◽  
pp. eabh0648
Author(s):  
Xing Mou ◽  
Jianshi Tang ◽  
Yingjie Lyu ◽  
Qingtian Zhang ◽  
Siyao Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
High Performance ◽  
Density Functional ◽  
Kinetic Monte Carlo ◽  
Random Access ◽  
Dual Mode ◽  
Neuromorphic Computing ◽  
Power Efficient ◽  
Network Pruning

Inspired by the human brain, nonvolatile memories (NVMs)–based neuromorphic computing emerges as a promising paradigm to build power-efficient computing hardware for artificial intelligence. However, existing NVMs still suffer from physically imperfect device characteristics. In this work, a topotactic phase transition random-access memory (TPT-RAM) with a unique diffusive nonvolatile dual mode based on SrCoOx is demonstrated. The reversible phase transition of SrCoOx is well controlled by oxygen ion migrations along the highly ordered oxygen vacancy channels, enabling reproducible analog switching characteristics with reduced variability. Combining density functional theory and kinetic Monte Carlo simulations, the orientation-dependent switching mechanism of TPT-RAM is investigated synergistically. Furthermore, the dual-mode TPT-RAM is used to mimic the selective stabilization of developing synapses and implement neural network pruning, reducing ~84.2% of redundant synapses while improving the image classification accuracy to 99%. Our work points out a new direction to design bioplausible memristive synapses for neuromorphic computing.

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