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Quaternary Synapses Network for Memristor-based Spiking Convolutional Neural Networks

Sheng-Yang Sun, Jiwei Li, Zhiwei Li, Husheng Liu, Haijun Liu, Qingjiang Li
2019 IEICE Electronics Express  
This paper proposes a method that renders the weights of the neural network with quaternary synapses map into the only four-level memristance of memristive devices.  ...  Systematic error analysis shows that the network can still reach over 95% accuracy under the condition of 95% yield of memristor crossbar array, 100 µV op-amp offset voltage and 0.5% Single-Pole-Double-Throw  ...  Memristor-based spiking convolutional neural networks with quaternary synapses SCNNs architecture The proposed architecture of SCNNs [12] is shown in Fig. 1 .  ... 
doi:10.1587/elex.16.20190004 fatcat:oiphsshmenew3d3e5clezqurta

Emerging neuromorphic devices

Daniele Ielmini, Stefano Ambrogio
2019 Nanotechnology  
Then, we describe the implementation of synaptic learning in the two main types of neural networks, namely the deep neural network and the spiking neural network (SNN).  ...  First, we provide an overview of the memory device technologies which have been proposed for synapse and neuron circuits in neuromorphic systems.  ...  Acknowledgments We acknowledge Valerio Milo for critical reading of the manuscript.  ... 
doi:10.1088/1361-6528/ab554b pmid:31698347 fatcat:txji4bp5tzh5tmjduwfj6d7gmy

On Ev-Degree and Ve-Degree Topological Properties of Tickysim Spiking Neural Network

Murat Cancan
2019 Computational Intelligence and Neuroscience  
Classical degree-based topological properties of Tickysim spiking neural network have been recently determined.  ...  Tickysim is a timing-based simulator of the interchip interconnection network of the SpiNNaker architecture. Tickysim spiking neural network is considered to be highly symmetrical network classes.  ...  Quaternary synapses network for memristor-based spiking convolutional neural networks has been investigated in [6] . Spiking neural p systems with learning functions have been proposed in [7] .  ... 
doi:10.1155/2019/8429120 pmid:31281340 pmcid:PMC6589261 fatcat:56r5xuv62veozliwtjckm7kb2y

In-materio neuromimetic devices: Dynamics, information processing and pattern recognition [article]

Dawid Przyczyna, Piotr Zawal, Tomasz Mazur, Pier Luigi Gentili, Konrad Szaciłowski
2020 arXiv   pre-print
Scheme of the emulated neural network (b).  ...  Figure 8 . 8 a) Spike waveforms, similar to biological neural spikes at different presynaptic firing frequency.  ... 
arXiv:2002.07712v1 fatcat:s7fgywfnrbhcrdndggeb7rulfu

Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications

Sourav De, Md. Aftab Baig, Bo-Han Qiu, Franz Müller, Hoang-Hiep Le, Maximilian Lederer, Thomas Kämpfe, Tarek Ali, Po-Jung Sung, Chun-Jung Su, Yao-Jen Lee, Darsen D. Lu
2022 Frontiers in Nanotechnology  
However, quaternary neural networks (QNNs) and binary neural networks (BNNs) with Fe-finFETs as synaptic devices demonstrated excellent immunity toward the cumulative impact of stochastic and systematic  ...  Statistical modeling of device-to-device and cycle-to-cycle variation is performed based on measured data and applied to neural network simulations using the CIMulator software platform.  ...  Introduction The advent of convolutional neural networks (Lecun et al., 2015) has made machine learning or neural networkbased computation an inevitable choice for solving many complex tasks in recent  ... 
doi:10.3389/fnano.2021.826232 fatcat:gg5x55rkhvdedlsztbkatswiqi

Training Spiking Neural Networks for Energy-Efficient Neuromorphic Computing

Gopalakrishnan Srinivasan
Spiking Neural Networks (SNNs), widely known as the third generation of artificial neural networks, offer a promising solution to approaching the brains' processing capability for cognitive tasks.  ...  A bio-plausible probabilistic-STDP learning rule consistent with Hebbian learning theory is proposed to train a network of binary as well as quaternary synapses.  ...  MAGNETIC TUNNEL JUNCTION BASED STOCHASTIC SPIKING NEURAL NETWORK FOR ENERGY-EFFICIENT NEUROMORPHIC COMPUTING 2.1 Introduction Spiking Neural Networks (SNNs) offer a promising solution for realizing  ... 
doi:10.25394/pgs.11336840.v1 fatcat:34m7x72gmjfatgjsqannjiefnu

2021 Index IEEE Transactions on Electron Devices Vol. 68

2021 IEEE Transactions on Electron Devices  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TED Sept. 2021 4801-4804 A Highly Scalable Junctionless FET Leaky Integrate-and-Fire Neuron for Spiking Neural Networks.  ...  Dhillon, H., +, TED Nov. 2021 5498-5503 Feedback Hardware-Based Spiking Neural Networks Using Capacitor-Less Positive Feedback Neuron Devices.  ... 
doi:10.1109/ted.2021.3138305 fatcat:37sowz27xjc4bjhktlrldi2nja