Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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Journal ArticleDOI
Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networks
TL;DR: A novel architecture optimized for the processing of convolutional SNNs (CSNNs) featuring a high degree of sparsity is proposed and implemented on an FPGA and achieved a significant speedup compared to previously proposed SNN implementations while needing less hardware resources and maintaining a higher energy efficiency.
Proceedings ArticleDOI
Efficient Techniques for Training the Memristor-based Spiking Neural Networks Targeting Better Speed, Energy and Lifetime
Yu Ma,Pingqiang Zhou +1 more
TL;DR: In this paper, a model tuning technique for memristor-based crossbar circuit was proposed to optimize the weight and bias of a given SNN, which can reduce about 587% crossbar energy consumption and over 625% time consumption.
Journal ArticleDOI
tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks
TL;DR: The tinySNN effectively compresses the given SNN model to achieve high accuracy in a memory- and energy-efficient manner, hence enabling the employment of SNNs for the resource-and energy-constrained embedded applications.
Journal ArticleDOI
Deep Spike Learning With Local Classifiers
TL;DR: In this paper , the authors proposed a spike-based efficient local learning rule by only considering the direct dependencies in the current time and two variants that additionally incorporate temporal dependencies through a backward and forward process, respectively.
Proceedings ArticleDOI
Storing and Retrieving Wavefronts with Resistive Temporal Memory
Advait Madhavan,Mark D. Stiles +1 more
TL;DR: A spike timing dependent plasticity (STDP) inspired wavefront recording scheme to capture incoming wavefronts and the effects of memristor non-idealities on the operation of such a memory are analyzed.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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