An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks

Chen, Xiangyu; Yajima, Takeaki; Inoue, Isao; Iizuka, Tetsuya

doi:10.35848/1347-4065/ac43e4

Cited by 5 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[25] As for the action mechanism of biological neurons depicted in Figure 3a, if the neurons receive sufficient input spikes from the adjacent neurons through joint synapses, its membrane potential may exceed a certain membrane potential threshold, leading to the transmition of the spike stimuli into the axon, which is often referred to as output firing. [6] Simply put, one fundamental function of biological neurons, that is, the complete realization of information exchange, can be achieved by receiving, integrating, transmitting, and outputting information. [26] It has been widely reported that the CFs-based TS devices can be used in realizing various neuronal characteristics.…”

Section: Resultsmentioning

confidence: 99%

“…[5] Structurally speaking, a hardware friendly and energy efficient SNN is composed of two fundamental information processing elements, that is, artificial neurons and synapses, which need to meet the requirements of high-density integration and excellent stability. [6,7] In order to guarantee the synapse matrix works effectively, volatile threshold switching (TS) devices are needed to simulate integrate-and-fire function of neurons. [8] Nevertheless, many neural devices used for SNNs are the mainstream complementary metal-oxidesemiconductor (CMOS) neurons that are indispensable for realizing of sophisticated circuits, which are considered unsuitable for large-scale network implementation due to their large area and low efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neurons Using Ag−In−Zn−S/Sericin Peptide‐Based Threshold Switching Memristors for Spiking Neural Networks

He,

Yan,

Zhang

et al. 2023

Adv Elect Materials

View full text Add to dashboard Cite

Memristive devices with threshold switching characteristics can be effectively utilized to mimic biological neurons acting as one of the key building blocks for constructing advanced hardware neural networks. In this work, the emulation of leaky integrate‐and‐fire memristive neuron is realized in one single cell with Ag/Ag−In−Zn−S/silk sericin/W architecture without the need for additional auxiliary circuits. The studied devices demonstrate excellent electrical properties, such as stably repeatable threshold switching, concentratedly low threshold voltage (≈0.4 V), and relatively small device‐to‐device variation. In addition, multiple neural features, such as leaky integrate‐and‐fire neuron functionality and strength‐modulated spike frequency characteristic, have been successfully emulated owing to the forming‐free volatile threshold switching effect. The stable volatile threshold switching behaviors and regular firing event may be attributed to the controllable metallic Ag filamentary mechanism. Furthermore, a solid accuracy of 91.44% of the pattern recognition of Modified National Institute of Standards and Technology (MNIST) data is obtained via a trained spiking neural network (SNN) based on the leaky integrate‐and‐fire behavior of sericin‐based device. These achievements shed light on the fact that employing sericin biomaterials has great application potential in advanced neuromorphic computation.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neurons Using Ag−In−Zn−S/Sericin Peptide‐Based Threshold Switching Memristors for Spiking Neural Networks

He,

Yan,

Zhang

et al. 2023

Adv Elect Materials

View full text Add to dashboard Cite

show abstract

Effects of Noise on Leaky Integrate-and-Fire Neuron Models for Neuromorphic Computing Applications

Thieu

Melnik

2022

Computational Science and Its Applications – ICCSA 2022

View full text Add to dashboard Cite

CMOS-based area-and-power-efficient neuron and synapse circuits for time-domain analog spiking neural networks

Chen

Byambadorj

Yajima

et al. 2023

Applied Physics Letters

View full text Add to dashboard Cite

Conventional neural structures tend to communicate through analog quantities, such as currents or voltages; however, as CMOS devices shrink and supply voltages decrease, the dynamic range of voltage/current-domain analog circuits becomes narrower, the available margin becomes smaller, and noise immunity decreases. More than that, the use of operational amplifiers (op-amps) and continuous-time or clocked comparators in conventional designs leads to high energy consumption and large chip area, which would be detrimental to building spiking neural networks. In view of this, we propose a neural structure for generating and transmitting time-domain signals, including a neuron module, a synapse module, and two weight modules. The proposed neural structure is driven by a leakage current of MOS transistors and uses an inverter-based comparator to realize a firing function, thus providing higher energy and area efficiency compared to conventional designs. The proposed neural structure is fabricated using a TSMC 65 nm CMOS technology. The proposed neuron and synapse occupy the area of 127 and 231 [Formula: see text], respectively, while achieving millisecond time constants. Actual chip measurements show that the proposed structure implements the temporal signal communication function with millisecond time constants, which is a critical step toward hardware reservoir computing for human–computer interaction. Simulation results of the spiking neural network for reservoir computing with the behavioral model of the proposed neural structure demonstrates the learning function.

show abstract

An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks

Cited by 5 publications

References 49 publications

Artificial Neurons Using Ag−In−Zn−S/Sericin Peptide‐Based Threshold Switching Memristors for Spiking Neural Networks

Artificial Neurons Using Ag−In−Zn−S/Sericin Peptide‐Based Threshold Switching Memristors for Spiking Neural Networks

Effects of Noise on Leaky Integrate-and-Fire Neuron Models for Neuromorphic Computing Applications

CMOS-based area-and-power-efficient neuron and synapse circuits for time-domain analog spiking neural networks

Contact Info

Product

Resources

About