Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

Jiang, Wenwu; Li, Jie; Liu, Hongbo; Qian, Xicong; Ge, Yuan; Wang, Lidan; Duan, Shukai

doi:10.1088/1674-1056/ac380b

Cited by 9 publications

(5 citation statements)

References 42 publications

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“…Recently, several models belonging to SNNs (Xu et al 2021;Wang and Jiang 2022;Zhu et al 2022) have been studied with low energy consumption and high bio-fidelity. They directly utilize the same-reactive spiking neurons (Gerstner and Kistler 2002) to process the propagated graph data with maintaining a low energy consumption and certain bio-fidelity.…”

Section: Graph Neural Networkmentioning

confidence: 99%

“…Thus, to enable GNNs to satisfy the low-energy and high bio-fidelity, studying Spiking GNNs is necessary on the ubiquitous large-scale graph data. Existing methods (Xu et al 2021;Wang and Jiang 2022;Zhu et al 2022) directly replace the conventional GNNs' neurons with spiking ones for processing the propagated graph, where these spiking neurons have consistent firing thresholds with the same reactive state. According to the dynamic cognition observation that biological neurons have dynamic reactive states on processing signals (Deco, Cruzat, and Kringelbach 2019;Deco, Vidaurre, and Kringelbach 2021), such same-reactive neurons may have not enough capacity in terms of biological functionality, and thus limit the expressive ability of the model.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Reactive Spiking Graph Neural Network

Zhao,

Yang,

Deng

et al. 2024

AAAI

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Spiking Graph Neural Networks are emerging tools for analyzing graph data along with low energy consumption and certain biological fidelity. Existing methods directly integrate same-reactive spiking neurons into graph neural networks for processing propagated graphs. However, such same-reactive neurons are not biological-functionality enough compared to the brain's dynamic-reactive ones, limiting the model's expression. Meanwhile, insufficient long-range neighbor information can be excavated with the few-step propagated graph, restricting discrimination of graph spiking embeddings. Inspired by the dynamic cognition in the brain, we propose a Dynamic Reactive Spiking Graph Neural Network that can enhance model's expressive ability in higher biological fidelity. Specifically, we design dynamic reactive spiking neurons to process spiking graph inputs, which have unique optimizable thresholds to spontaneously explore dynamic reactive states between neurons. Moreover, discriminative graph positional spikes are learned and integrated adaptively into spiking outputs through our neurons, thereby exploring long-range neighbors more thoroughly. Finally, with the dynamic reactive mechanism and learnable positional integration, we can obtain a powerful and highly bio-fidelity model with low energy consumption. Experiments on various domain-related datasets can demonstrate the effectiveness of our model. Our code is available at https://github.com/hzhao98/DRSGNN.

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Section: Graph Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Reactive Spiking Graph Neural Network

Zhao,

Yang,

Deng

et al. 2024

AAAI

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“…Understanding how the brain works can provide valuable insights for creating new structures and systems. Therefore, developing brain-inspired intelligent devices and constructing brain-inspired computing systems are critical to breaking through existing bottlenecks [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Research Progress of Neural Synapses Based on Memristors

Chen

et al. 2023

Electronics

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The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable resistance, is a promising candidate for constructing brain-inspired computing. It operates based on ion migration, enabling it to store and retrieve electrical charges. This paper reviews current research on synapses using digital and analog memristors. Synapses based on digital memristors have been utilized to construct positive, zero, and negative weights for artificial neural networks, while synapses based on analog memristors have demonstrated their ability to simulate the essential functions of neural synapses, such as short-term memory (STM), long-term memory (LTM), spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), and paired-pulse facilitation (PPF). Furthermore, synapses based on analog memristors have shown potential for performing advanced functions such as experiential learning, associative learning, and nonassociative learning. Finally, we highlight some challenges of building large-scale artificial neural networks using memristors.

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“…Memristor is a two-terminal nonvolatile electronic device. [9][10][11][12] Its resistance state can be switched between the low resistance state (LRS) and the high resistance state (HRS). The switching rule depends on the magnitude and the polarity of the applied voltage on it.…”

Section: Introductionmentioning

confidence: 99%

High throughput N-modular redundancy for error correction design of memristive stateful logic

Zhu¹,

Xu²,

Yang³

et al. 2023

Chinese Phys. B

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Memristive stateful logic is one of the most promising candidates to implement an in-memory computing system that computes within the storage unit. It can eliminate the costs for the data movement in the traditional von Neumann system. However, the instability in the memristors is inevitable due to the limitation of the current fabrication technology, which incurs a great challenge for the reliability of the memristive stateful logic. In this paper, the implication of device instability on the reliability of the logic event is simulated. The mathematical relationship between logic reliability and redundancy has been deduced. By combining the mathematical relationship with the vector-matrix multiplication in a memristive crossbar array, the logic error correction scheme with high throughput has been proposed. Moreover, a universal design paradigm has been put forward for complex logic. And the circuit schematic and the flow of the scheme have been raised. Finally, A 1-bit full adder (FA) based on the NOR logic and NOT logic is simulated and the mathematical evaluation is performed. It demonstrates the scheme can improve the reliability of the logic significantly. And compared with other four error corrections, the scheme which can be suitable for all kinds of R-R logics and V-R logics has the best universality and throughput. Compared with the other two approaches which also need additional CMOS circuits, it needs fewer transistors and cycles for the error correction.

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Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

Cited by 9 publications

References 42 publications

Dynamic Reactive Spiking Graph Neural Network

Dynamic Reactive Spiking Graph Neural Network

Research Progress of Neural Synapses Based on Memristors

High throughput N-modular redundancy for error correction design of memristive stateful logic

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