Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

Sengupta, Abhronil; Panda, Priyadarshini; Wijesinghe, Parami; Kim, Yusung; Roy, Kaushik

doi:10.1038/srep30039

Cited by 151 publications

(119 citation statements)

References 35 publications

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“…The stochastic switching behavior of the nano-magnets have been exploited for random number generation32 and in neuromorphic applications33. However, in the present scenario for Boolean logic gates, we need deterministic switching process.…”

Section: Device Characteristicsmentioning

confidence: 99%

MESL: Proposal for a Non-volatile Cascadable Magneto-Electric Spin Logic

Jaiswal

Roy

2017

Sci Rep

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In the quest for novel, scalable and energy-efficient computing technologies, many non-charge based logic devices are being explored. Recent advances in multi-ferroic materials have paved the way for electric field induced low energy and fast switching of nano-magnets using the magneto-electric (ME) effect. In this paper, we propose a voltage driven logic-device based on the ME induced switching of nano-magnets. We further demonstrate that the proposed logic-device, which exhibits decoupled read and write paths, can be used to construct a complete logic family including XNOR, NAND and NOR gates. The proposed logic family shows good scalability with a quadratic dependence of switching energy with respect to the switching voltage. Further, the proposed logic-device has better robustness against the effect of thermal noise as compared to the conventional current driven switching of nano-magnets. A device-to-circuit level coupled simulation framework, including magnetization dynamics and electron transport model, has been developed for analyzing the present proposal. Using our simulation framework, we present energy and delay results for the proposed Magneto-Electric Spin Logic (MESL) gates.

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Section: Device Characteristicsmentioning

confidence: 99%

MESL: Proposal for a Non-volatile Cascadable Magneto-Electric Spin Logic

Jaiswal

Roy

2017

Sci Rep

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“…[20][21][22] All these memory types, except SRAM, are nonvolatile, i.e., a memory device maintains the stored data even without having power supply. [23][24][25][26] Racetrack memory is known for its extremely high density (20 nm-wide nanowire) and the sequential access along tracks. [8][9][10] Besides persistent data storage, nonvolatile memory technologies generally have a higher density, i.e., <100 F 2 cell size, where F represents the technology feature size.…”

Section: Introductionmentioning

confidence: 99%

Resistive Memory‐Based In‐Memory Computing: From Device and Large‐Scale Integration System Perspectives

Yan

Qiao

et al. 2019

Advanced Intelligent Systems

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In‐memory computing is a computing scheme that integrates data storage and arithmetic computation functions. Resistive random access memory (RRAM) arrays with innovative peripheral circuitry provide the capability of performing vector‐matrix multiplication beyond the basic Boolean logic. With such a memory–computation duality, RRAM‐based in‐memory computing enables an efficient hardware solution for matrix‐multiplication‐dependent neural networks and related applications. Herein, the recent development of RRAM nanoscale devices and the parallel progress on circuit and microarchitecture layers are discussed. Well suited for analog synapse and neuron implementation, RRAM device properties and characteristics are emphasized herein. 3D‐stackable RRAM and on‐chip training are introduced in large‐scale integration. The circuit design and system organization of RRAM‐based in‐memory computing are essential to breaking the von Neumann bottleneck. These outcomes illuminate the way for the large‐scale implementation of ultra‐low‐power and dense neural network accelerators.

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“…In memory applications, reasonably high current is applied to devices based on nanomagnets to achieve deterministic switching across a range of temperature. Recently, however, the stochasticity introduced by the temperature dependence of nanomagnetic devices has been leveraged to implement several low-input applications such as biased random number generator [1], Boolean and non-Boolean computations [2], spiking neural networks [3] and more recently, optimization based on Ising computations [4]. Recently, devices based on superparamagnetic magnets, with low energy-barrier (E B ∼ 1kT ) between the two magnetic states, have been experimentally demonstrated to perform ultra-low power computations [5] at the rate of tens of MHz [6] which can potentially go up to gigahertz range.…”

Section: Introductionmentioning

confidence: 99%

Design of a Low-Voltage Analog-to-Digital Converter Using Voltage-Controlled Stochastic Switching of Low Barrier Nanomagnets

2018

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The inherent stochasticity makes nanoscale devices prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic behavior. Recently, superparamagnetic nanomagnets (having low energy barrier EB ≈ 1kT ) have shown promise of achieving stochastic switching at gigahertz rates, with very low currents. On the other hand, voltage-controlled switching of nanomagnets through the Magneto-electric (ME) effect has shown further improvements in energy efficiency. In this simulation paper, we model a voltage controlled spintronic device based on superparamagnetic nanomagnets exhibiting telegraphic switching characteristics and analyze its behavior under the influence of external bias. Subsequently, we show that the device leverages the voltage controlled stochasticity in performing low-voltage 8-bit analog to digital conversions. This eliminates the need for comparators, unlike the CMOS-based Flash Analog-to-Digital converters (ADC). This device allows for a simple and compact design of low-power approximate ADCs, which have become a quintessential element with the recent developments in neuromorphic computing and "Internet of Things".

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Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

Cited by 151 publications

References 35 publications

MESL: Proposal for a Non-volatile Cascadable Magneto-Electric Spin Logic

MESL: Proposal for a Non-volatile Cascadable Magneto-Electric Spin Logic

Resistive Memory‐Based In‐Memory Computing: From Device and Large‐Scale Integration System Perspectives

Design of a Low-Voltage Analog-to-Digital Converter Using Voltage-Controlled Stochastic Switching of Low Barrier Nanomagnets

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