Brain-Inspired Spiking Neural Network Using Superconducting Devices

Zhang, Huilin; Chen, Gang; Chen, Xu; Gong, Guoliang; Lu, Huaxiang

doi:10.1109/tetci.2021.3089328

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…Recently, some researchers have constructed a simplified brain-inspired SNN architecture through superconducting hardware materials or brain-inspired chips to calculate the actual energy consumption value [29,30,31]. Since our research does not include hardware aspects, the SNN model constructed in this paper cannot calculate the actual energy consumption value.…”

Section: Network Computing Energy Consumption Estimationmentioning

confidence: 99%

“…Inspired by biological neural networks, which have significant advantages in energy consumption, parallel processing, and spatiotemporal computing, researchers realize neuromorphic computing or brain-inspired computing by constructing a brain-inspired spiking neural network (SNN) [29,30,31]. Compared with the artificial neural network (such as CNN), the design principle of SNN is closer to the brain-inspired mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Liu

Cao

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Recently, deep learning methods have made significant progress in solving hyperspectral images (HSIs) classification problems of high-dimensional features, band redundancy, and spectral mixture. However, the deep neural network is too complex, with a long training time and high energy consumption, making it difficult to deploy on edge computing devices. In order to solve the above problems, this paper proposes a brain-inspired computing framework based on the spiking leaky integrate-andfire neuron model for HSIs classification. Then we design an approximate derivative algorithm to solve the non-differentiable spike activity of the spiking neuron. The framework uses direct coding to generate spatiotemporal spikes for input HSI and achieves efficient extraction of spatial-spectral features through spiking standard convolution and spiking depthwise separable convolution. Extensive experiments are performed on four benchmark hyperspectral data sets and two public unmanned aerial vehicle-borne hyperspectral data sets. Experiments show that the proposed model has the advantages of high classification accuracy and fewer spiking time steps. The proposed model can save about 10 times computational energy consumption compared with the CNN of the same architecture. This research has great significance for overcoming the technical bottleneck of HSI classification based on brain-inspired computing, solving the critical problems of mobile computing in unmanned autonomous systems, and realizing the engineering application of unmanned aerial vehicles and software-defined satellites. The source code will be made available at https://github.com/Katherine-Cao/HSI SNN.

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Section: Network Computing Energy Consumption Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Liu

Cao

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Spike rate coding can be viewed as a quantitative measure of neuronal output. [125] The temporal coding method records the firing time of the neuron's first pulse. After the pulse is generated, the neuron is inhibited until the next stimulus arrives so that the temporal spike limits the computing power of neurons.…”

Section: Spiking Codingmentioning

confidence: 99%

Integrated Memristor Network for Physiological Signal Processing

Cai

Yue

et al. 2023

Adv Elect Materials

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Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks.

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“…In addition to these suggested applications, the stochastic nature of occurrence of coherent quantum phase-slips in nanowires can be particularly applicable for neuromorphic computing. Recently, there have been promising results for an algorithm-level, digit recognition approach using models for QPSJ-based superconductive circuitry, which furthermore shows the growing interest in this area (Zhang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions

et al. 2021

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We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.

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Brain-Inspired Spiking Neural Network Using Superconducting Devices

Cited by 11 publications

References 34 publications

Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Integrated Memristor Network for Physiological Signal Processing

Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions

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