Token-passing communication protocol in hardware-based real-time spiking neural networks

2009 ◽  
Author(s):  
B. Belhadj ◽  
J. Tomas ◽  
O. Malot ◽  
Y. Bornat ◽  
G. N'Kaoua ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Communication Protocol ◽  
Spiking Neural Networks ◽  
Token Passing

Real-time multi-board architecture for analog spiking neural networks

2010 ◽  
Author(s):  
Sylvain Saighi ◽  
Jean Tomas ◽  
Yannick Bornat ◽  
Bilel Belhadj ◽  
Olivia Malot ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Spiking Neural Networks

Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks

2019 ◽  
Vol 49 (7) ◽  
pp. 2490-2503 ◽  
Author(s):  
Shuangming Yang ◽  
Jiang Wang ◽  
Bin Deng ◽  
Chen Liu ◽  
Huiyan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Large Scale ◽  
Spiking Neural Networks ◽  
Neuromorphic System

scholarly journals Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

2016 ◽  
Vol 10 ◽  
Author(s):  
Thilo Werner ◽  
Elisa Vianello ◽  
Olivier Bichler ◽  
Daniele Garbin ◽  
Daniel Cattaert ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Spiking Neural Networks ◽  
Spike Sorting

scholarly journals Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1059 ◽  
Author(s):  
Wenzhe Guo ◽  
Hasan Erdem Yantır ◽  
Mohammed E. Fouda ◽  
Ahmed M. Eltawil ◽  
Khaled Nabil Salama
Keyword(s):  
Neural Networks ◽  
Energy Efficiency ◽  
Real Time ◽  
Classification Accuracy ◽  
Energy Efficient ◽  
Spiking Neural Networks ◽  
Neuron Network ◽  
Neuromorphic Hardware ◽  
Neuromorphic Systems ◽  
Network Compression

To solve real-time challenges, neuromorphic systems generally require deep and complex network structures. Thus, it is crucial to search for effective solutions that can reduce network complexity, improve energy efficiency, and maintain high accuracy. To this end, we propose unsupervised pruning strategies that are focused on pruning neurons while training in spiking neural networks (SNNs) by utilizing network dynamics. The importance of neurons is determined by the fact that neurons that fire more spikes contribute more to network performance. Based on these criteria, we demonstrate that pruning with an adaptive spike count threshold provides a simple and effective approach that can reduce network size significantly and maintain high classification accuracy. The online adaptive pruning shows potential for developing energy-efficient training techniques due to less memory access and less weight-update computation. Furthermore, a parallel digital implementation scheme is proposed to implement spiking neural networks (SNNs) on field programmable gate array (FPGA). Notably, our proposed pruning strategies preserve the dense format of weight matrices, so the implementation architecture remains the same after network compression. The adaptive pruning strategy enables 2.3× reduction in memory size and 2.8× improvement on energy efficiency when 400 neurons are pruned from an 800-neuron network, while the loss of classification accuracy is 1.69%. And the best choice of pruning percentage depends on the trade-off among accuracy, memory, and energy. Therefore, this work offers a promising solution for effective network compression and energy-efficient hardware implementation of neuromorphic systems in real-time applications.

scholarly journals Real-time detection of bursts in neuronal cultures using a Neuromorphic Auditory Sensor and Spiking Neural Networks

2021 ◽  
Author(s):  
Juan P. Dominguez-Morales ◽  
Stefano Buccelli ◽  
Daniel Gutierrez-Galan ◽  
Ilaria Colombi ◽  
Angel Jimenez-Fernandez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Spiking Neural Networks ◽  
Neuronal Cultures ◽  
Real Time Detection

scholarly journals Corrigendum: Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

2017 ◽  
Vol 11 ◽  
Author(s):  
Thilo Werner ◽  
Elisa Vianello ◽  
Olivier Bichler ◽  
Daniele Garbin ◽  
Daniel Cattaert ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Spiking Neural Networks ◽  
Spike Sorting
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