An ultra-compact and low power neuron based on SOI platform

Ostwal, Vaibhav; Meshram, Rahul; Rajendran, Bipin; Ganguly, Udayan

doi:10.1109/vlsi-tsa.2015.7117569

Cited by 19 publications

(11 citation statements)

References 5 publications

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“…To add the fire and reset, a current threshold I th is set such that when I D exceeds I th , we set V DG = 0 manually. This can be automatically performed by an external circuit as explained earlier 8 . Figure 5(a) shows the reset effect where V DG is set to zero if I ≥ I th = 500 μA manually.…”

Section: Experimental Validationmentioning

confidence: 99%

“…Second, the technology must be sufficiently matured to enable extreme integration of numerous (~10 11 ) neuron. Recently, our group has proposed a highly power and area efficient neuron on impact ionization based n+/p/n+ diode (I-NPN) device with an extended gate driven by a small reset circuit in a device simulations study 8 . Excellent area (60x) and power improvement (5x) is demonstrated compared to previously reported analog circuits 8 .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, our group has proposed a highly power and area efficient neuron on impact ionization based n+/p/n+ diode (I-NPN) device with an extended gate driven by a small reset circuit in a device simulations study 8 . Excellent area (60x) and power improvement (5x) is demonstrated compared to previously reported analog circuits 8 . Further, record low bias (sub-0.2 V) impact ionization in I-NPN is also experimentally demonstrated by our group 9 .…”

Section: Introductionmentioning

confidence: 99%

“…The I ( t ) ≥ I th condition sets off a small “reset circuit” that removes drain bias ( V DG = 0) disabling II (i.e. I II − h → 0) 8 . Hence, all the stored holes leak ( I leak − h ) through both the source and the drain junction (Fig.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET

Dutta

Kumar

Shukla

et al. 2017

Sci Rep

160

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Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~1011 neuron based) large neural networks.

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Section: Experimental Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET

Dutta

Kumar

Shukla

et al. 2017

Sci Rep

160

View full text Add to dashboard Cite

show abstract

“…However, complementary metal-oxide semiconductor (CMOS) based neuron circuits for complex neuron functions require many transistors and large capacitors including a membrane capacitor ( C mem ) in a neuron circuit leading to a large area. Therefore, to implement a high density SNN, neuron devices with the integration capability have recently been reported, such as impact-ionization based NPN selector (I-NPN) and NPN device with an extended gate (gated-INPN) on a silicon-on-insulator (SOI) platform ( Ostwal et al, 2015 ; Dutta et al, 2017 ), spin-transfer torque (STT) devices based on a magnetic tunnel junction (MTJ) ( Sharad et al, 2012 ; Zhang et al, 2016 ) and memristor ( Wang et al, 2018 ). However, the neuron circuit based on the SOI devices using the impact-ionization has the body-floated CMOS FETs, and their performance can be degraded by the floating-body effect ( Vandana, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit

et al. 2018

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Hardware-based spiking neural networks (SNNs) to mimic biological neurons have been reported. However, conventional neuron circuits in SNNs have a large area and high power consumption. In this work, a split-gate floating-body positive feedback (PF) device with a charge trapping capability is proposed as a new neuron device that imitates the integrate-and-fire function. Because of the PF characteristic, the subthreshold swing (SS) of the device is less than 0.04 mV/dec. The super-steep SS of the device leads to a low energy consumption of ∼0.25 pJ/spike for a neuron circuit (PF neuron) with the PF device, which is ∼100 times smaller than that of a conventional neuron circuit. The charge storage properties of the device mimic the integrate function of biological neurons without a large membrane capacitor, reducing the PF neuron area by about 17 times compared to that of a conventional neuron. We demonstrate the successful operation of a dense multiple PF neuron system with reset and lateral inhibition using a common self-controller in a neuron layer through simulation. With the multiple PF neuron system and the synapse array, on-line unsupervised pattern learning and recognition are successfully performed to demonstrate the feasibility of our PF device in a neural network.

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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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An ultra-compact and low power neuron based on SOI platform

Cited by 19 publications

References 5 publications

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET

A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit

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

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