Experimental Demonstration of Ferroelectric Spiking Neurons for Unsupervised Clustering

Neuromorphic or bioinspired computational platforms, as an alternative for von-Neumann structures, have benefitted from the excellent features of emerging technologies in order to emulate the behaviour of biological brain in an accurate and energy efficient way. Integrability with CMOS technology and low power consumption make Ferroelectric FET (FEFET) an attractive candidate to perform such paradigms, particularly for image processing. In this paper, we use FEFET device to make energy-efficient oscillatory neurons as main parts of neural networks for image processing applications, especially for edge-detection. Based on our simulation results, we estimated a significant energy efficiency compared to other technologies which shows roughly − × reduction, depended on the design.

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“…To evaluate the frequency behaviour of ferroelectric oscillator neurons we use the neuron circuit, reported in [30], and shown in Fig. 2(a).…”

Section: Fefet-based Oscillator Neuronsmentioning

confidence: 99%

Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

Eslahi

Hamilton

Khandelwal

2020

IEEE J. Explor. Solid-State Comput. Devices Circuits

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“…The network training process is based on the standard STDP mechanism [14,16,[19][20][21]38]. This mechanism is an implementation of the Hebbian learning rule and causes a change in synaptic weight depending on the delay ∆t in-out between pre-synaptic and post-synaptic spikes [45].…”

Section: Snn Trainingmentioning

confidence: 99%

“…In other cases, the resistance Rw_i, j increases. Typically, the STDP-based training procedure is used in SNN with unsupervised learning [19][20][21]42,44]. In this case, when training input data is supplied, the output neurons are randomly associated with input data patterns.…”

Section: Snn Trainingmentioning

confidence: 99%

“…Although such SNNs currently provide impressive performance results [14], any emulation loses hardware implementation in performance and energy efficiency [15,16]. Therefore, the development of SNNs based on microelectronic elements attracts the active attention of researchers [16][17][18][19][20][21][22]. One of the most frequently used functional elements of SNN is a memristor [16], which is used for implementing customizable weights and as a functional element of a neuron.…”

Section: Introductionmentioning

confidence: 99%

“…The coding of information in the proposed network is performed by setting the delay time of the spikes in the input layer relative to the zero time moment (time to the first spike) [37]. Network training is performed according to the spike time-dependent plasticity (STDP) scheme [14,16,[19][20][21]38]. As a result, a model SNN, based on VO 2 neurons, which allows pattern recognition, is presented and investigated in this study.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Spiking Neural Network Based on the Model of VO2–Neuron

Belyaev

Velichko

2019

Electronics

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In this paper, we present an electrical circuit of a leaky integrate-and-fire neuron with one VO2 switch, which models the properties of biological neurons. Based on VO2 neurons, a two-layer spiking neural network consisting of nine input and three output neurons is modeled in the SPICE simulator. The network contains excitatory and inhibitory couplings, and implements the winner-takes-all principle in pattern recognition. Using a supervised Spike-Timing-Dependent Plasticity training method and a timing method of information coding, the network was trained to recognize three patterns with dimensions of 3 × 3 pixels. The neural network is able to recognize up to 105 images per second, and has the potential to increase the recognition speed further.

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Single‐Transistor Optoelectronic Spiking Neuron with Optogenetics‐Inspired Spatiotemporal Dynamics

Li,

Hu,

Zhang

et al. 2024

Adv Funct Materials

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The visual system is crucial for human perception and learning, and the retina is responsible for pre‐processing visual information. Mimicking the neurobiological functions of the retina provides the basis for neuromorphic vision hardware. Here, an optogenetics‐inspired prototype capable of modifying neuron‐spiking behavior in a retina‐like manner is demonstrated. In addition to basic information processing, neuronal signal transmission in the retina is bio‐faithfully emulated using optical modulation, that is, neuronal firing is inhibited in the dark and activated upon illumination. The proposed single threshold‐switch field‐effect transistor optoelectronic neuron features an ultra‐compact design by integrating a gate‐modulated 2D MoS2 channel with a volatile threshold switch, and optoelectronic performance is achieved through a multi‐physics resistance‐matching mechanism. This study provides a promising pathway toward highly scalable and hierarchically designed spiking electronics, expanding the spectrum of neuromorphic hardware applications in the emerging field of biomimetic vision.

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Experimental Demonstration of Ferroelectric Spiking Neurons for Unsupervised Clustering

Cited by 80 publications

References 5 publications

Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

A Spiking Neural Network Based on the Model of VO2–Neuron

Single‐Transistor Optoelectronic Spiking Neuron with Optogenetics‐Inspired Spatiotemporal Dynamics

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