A Highly Scalable Junctionless FET Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Kamal, Neha; Singh, Jawar

doi:10.1109/ted.2021.3061036

Cited by 38 publications

(10 citation statements)

References 27 publications

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“…There were a few attempts to construct an artificial neuron by use of a metal–oxide–semiconductor field‐effect transistor (MOSFET) neuron accompanied with supportive circuits by use of CMOS fabrication. [ 34–36 ] They used the impact ionization to create electron–hole pairs. Then the generated holes were accumulated in a floating body of the above MOSFET.…”

Section: Artificial Neuron Devices For a Spiking Neural Networkmentioning

confidence: 99%

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Han

Yun

Lee

et al. 2022

Adv Funct Materials

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A spiking neural network (SNN) inspired by the structure and principles of the human brain can significantly enhance the energy efficiency of artificial intelligence computing by overcoming the bottlenecks of the conventional von Neumann architecture with its massive parallelism and spike transmissions. The construction of artificial neurons is important for the hardware implementation of an SNN, which generates spike signals when enough synaptic signals are gathered. Because circuit‐level artificial neurons with comparator and reset circuits require considerable hardware area, intensive efforts are devoted in recent years for building artificial neurons at the device level for better area efficiency. Furthermore, artificial sensory neuron devices, which perform neural processing and sensing concurrently, have recently been developed in order to reduce the hardware cost and energy consumption of traditional sensory systems through in‐sensor computing. This review article surveys and benchmarks the recent progress of artificial neuron devices for neural processing and sensing. First, various artificial neuron devices are summarized, including single‐transistor neurons (1T‐neurons), memristor neurons, phase‐change neurons, magnetic neurons, and ferroelectric neurons. Next, cointegration technologies with artificial synaptic devices and artificial sensory neurons for in‐sensor computing are introduced. Finally, the challenges and prospects for developing artificial neuron devices are discussed.

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Section: Artificial Neuron Devices For a Spiking Neural Networkmentioning

confidence: 99%

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Han

Yun

Lee

et al. 2022

Adv Funct Materials

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“…Authors in [21] have simulated a low-power-CMOS-based memristive. Recent studies such as in [64][65][66][68][69][70], revolve around innovative methods at the device level, such as bulk FINFET, partially depleted SOI MOSFET, L-shaped gate bipolar impact ionization MOS, double-gate junctionless FET, Ge-MOSFET, and multi-domain spintronic device for attaining lower E/spike metric for the LIF neurons. Our work conveys energy efficiency from the architectural, circuit, and algorithmic levels.…”

Section: System-level Comparison Between Digital and Mixed Scheme (Ds...mentioning

confidence: 99%

Acoustic scene analysis using analog spiking neural network

Mukhopadhyay

Naligala

Duggisetty

et al. 2022

Neuromorph. Comput. Eng.

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Sensor nodes in a wireless sensor network (WSN) for security surveillance applications should preferably be small, energy-efficient, and inexpensive with in-sensor computational abilities. An appropriate data processing scheme in the sensor node reduces the power dissipation of the transceiver through the compression of information to be communicated. This study attempted a simulation-based analysis of human footstep sound classification in natural surroundings using simple time-domain features. The spiking neural network (SNN), a computationally low-weight classifier derived from an artificial neural network (ANN), was used to classify acoustic sounds. The SNN and required feature extraction schemes are amenable to low-power subthreshold analog implementation. The results show that all analog implementations of the proposed SNN scheme achieve significant power savings over the digital implementation of the same computing scheme and other conventional digital architectures using frequency-domain feature extraction and ANN-based classification. The algorithm is tolerant of the impact of process variations, which are inevitable in analog design, owing to the approximate nature of the data processing involved in such applications. Although SNN provides low-power operation at the algorithm level, ANN to SNN conversion leads to an unavoidable loss of classification accuracy of ~5%. We exploited the low-power operation of the analog processing SNN module by applying redundancy and majority voting, which improved the classification accuracy, taking it close to the ANN model.

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“…Consequently, neural devices with spike-generating capabilities have been studied. [10][11][12][13][14][15][16] Specifically, neuron circuits that leverage floating body effects in a silicon-on-insulator FET (SOI-FET), which is used for both membrane capacitor and comparator functions, can reduce both the spike generation power and circuit footprint. [17][18][19] Recently, single-device SOI-FETs achieving LIF behavior have been explored.…”

Section: Introductionmentioning

confidence: 99%

Leaky-integrate-and-fire operation with signal amplification using gate-controlled carrier-injection silicon-on-insulator transistor

Yonezaki,

Mori,

Ida

2024

Jpn. J. Appl. Phys.

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This work demonstrated leaky-integrate-and-fire (LIF) behavior using a gate-controlled carrier-injection silicon-on-insulator transistor (GCCI SOI-Tr) and confirmed its signal amplification capability. We believe that a large-scale spiking neural network requires neural devices capable of signal amplification to effectively drive multiple post-synaptic neurons. The GCCI SOI-Tr could realize a capacitorless LIF behavior with a gate-controlled weight parameter. In addition, significant signal amplification might be achieved by scaling the GCCI SOI-Tr.

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A Highly Scalable Junctionless FET Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Cited by 38 publications

References 27 publications

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Acoustic scene analysis using analog spiking neural network

Leaky-integrate-and-fire operation with signal amplification using gate-controlled carrier-injection silicon-on-insulator transistor

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