Quaternary synapses network for memristor-based spiking convolutional neural networks

Sun, Siqi; Li, Jiwei; Li, Zhiwei; Liu, Husheng; Liu, Haijun; Li, Qingjiang

doi:10.1587/elex.16.20190004

Cited by 9 publications

(6 citation statements)

References 31 publications

(30 reference statements)

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“…As the development of computing technology enables complex computations and huge data processing, research on artificial intelligence (AI) has been promoted. Furthermore, the interest in AI computing, which includes neural networks, has also increased [1][2][3][4][5][6]. The neural network is the set of transmissions of signals between numerous calculating units and memories through entangled connections, similarly to how the brain works with spikes, neurons, and synapses [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Intellino: Processor for Embedded Artificial Intelligence

et al. 2020

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.

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Section: Introductionmentioning

confidence: 99%

Intellino: Processor for Embedded Artificial Intelligence

et al. 2020

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“…For an overview of the SpiNNaker system architecture, we refer the interested reader to reference [5]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Cancan

2019

Computational Intelligence and Neuroscience

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Topological indices are indispensable tools for analyzing networks to understand the underlying topology of these networks. Spiking neural network architecture (SpiNNaker or TSNN) is a million-core calculating engine which aims at simulating the behavior of aggregates of up to a billion neurons in real time. 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. Classical degree-based topological properties of Tickysim spiking neural network have been recently determined. Ev-degree and ve-degree concepts are two novel degrees recently defined in graph theory. Ev-degree and ve-degree topological indices have been defined as parallel to their corresponding counterparts. In this study, we investigate the ev-degree and ve-degree topological properties of Tickysim spiking neural network. These calculations give the information about the underlying topology of Tickysim spiking neural network.

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“…A few network architectures [13]- [16] which utilize memristor crossbar arrays to perform convolution computation have been proposed, these frameworks cost a great deal of time and require high memory. Moreover, the fabrication technology of larger-scale memristive arrays is still not mature [17]- [19], however useful cascaded frameworks in real applications with memristor-based architectures have seldom been reported. Therefore, cascading small-scale arrays to achieve the neuromorphic computational ability that can be achieved by large-scale arrays, which is of great significance for promoting the application of memristorbased neuromorphic computing.…”

Section: Introductionmentioning

confidence: 99%

Cascaded Architecture for Memristor Crossbar Array Based Larger-Scale Neuromorphic Computing

Sun

et al. 2019

IEEE Access

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Multiply-accumulate calculations using a memristor crossbar array is an important method to realize neuromorphic computing. However, the memristor array fabrication technology is still immature, and it is difficult to fabricate large-scale arrays with high-yield, which restricts the development of memristor-based neuromorphic computing technology. Therefore, cascading small-scale arrays to achieve the neuromorphic computational ability that can be achieved by large-scale arrays, which is of great significance for promoting the application of memristor-based neuromorphic computing. To address this issue, we present a memristor-based cascaded framework with some basic computation units, several neural network processing units can be cascaded by this means to improve the processing capability of the dataset. Besides, we introduce a split method to reduce the pressure of the input terminal. Compared with VGGNet and GoogLeNet, the proposed cascaded framework can achieve 93.54% Fashion-MNIST accuracy and 86.64% CIFAR-10 accuracy under the 4.15M parameters. The extensive experiments with Ti/AlOx/TaOx/Pt we fabricated are conducted to show that the circuit simulation results can still provide a high recognition accuracy, and the recognition accuracy loss after circuit simulation can be controlled at around 0.39%. INDEX TERMS Cascaded neural networks, memristor, crossbar array, convolutional neural networks.

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Quaternary synapses network for memristor-based spiking convolutional neural networks

Cited by 9 publications

References 31 publications

Intellino: Processor for Embedded Artificial Intelligence

Intellino: Processor for Embedded Artificial Intelligence

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

Cascaded Architecture for Memristor Crossbar Array Based Larger-Scale Neuromorphic Computing

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