A fully hardware-based memristive multilayer neural network

Kiani, Fatemeh; Yin, Jungang; Wang, Zhongrui; Yang, Jianhua; Xia, Qiangfei

doi:10.1126/sciadv.abj4801

Cited by 54 publications

(40 citation statements)

References 47 publications

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“…Neurons are powerful information processing units and can perform highly complex nonlinear computations even in individual cells. [ 50–52 ] Hardware implementation of artificial neurons with similar capability is of great significance for the construction of neuromorphic systems; in this aspect, memristive devices are crucial for implementing neuronal functions in artificial neurons. [ 6,53,54 ]…”

Section: Resultsmentioning

confidence: 99%

“…Neurons are powerful information processing units and can perform highly complex nonlinear computations even in individual cells. [50][51][52] Hardware implementation of artificial neurons with similar capability is of great significance for the construction of neuromorphic systems; in this aspect, memristive devices are crucial for implementing neuronal functions in artificial neurons. [6,53,54] Among various neuron models that have been established to describe the complex dynamics of biological neurons, the LIF neuron is most widely used for both algorithms and hardware implementations of SNNs.…”

Section: Lif Neuronsmentioning

confidence: 99%

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A Bio‐Inspired Neuromorphic Sensory System

Wang

Wen

et al. 2022

Advanced Intelligent Systems

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The advent of the intelligent society leads to the exponential growth of information, imposing urgent requirements in a time‐ and energy‐efficient way to process information where data are generated. This issue can be addressed by the neuromorphic paradigm of computing inspired by biological sensory systems that build up the association between external stimuli and the response of an organism in real‐time; in the paradigm, a neuromorphic system is integrated with sensors to form an artificial sensory system. Herein, a neuromorphic sensory system with integrated capabilities of gas sensing, data storage, and processing is demonstrated. Leaky integrate‐and‐fire (LIF) neurons, the basic computing units in the system, are realized with volatile memristive device Pt/Ag/TaOx/Pt; sensory neurons, i.e., the LIF neurons connected with an array of gas sensors, detect gases and convert the chemical information of gases into neural spikes; synapses based on nonvolatile memristive device Pt/Ta/TaOx/Pt transmit the signals from sensory neurons to relay neurons according to synaptic weights, which are trained by the supervised spike‐rate dependent plasticity; relay neurons then process the signals from the synapses and classify gases. The approach of this work can also be applied to emulate other biological perceptions through the integration with different sensors.

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Section: Resultsmentioning

confidence: 99%

Section: Lif Neuronsmentioning

confidence: 99%

A Bio‐Inspired Neuromorphic Sensory System

Wang

Wen

et al. 2022

Advanced Intelligent Systems

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“…Phase 3. Error calculation: according to the subtraction circuit [11], the errors between outputs and targets can be obtained.…”

Section: B Hardware-friendly Training Methodsmentioning

confidence: 99%

“…Phase 4. Gradient calculation: the obtained errors are applied into the gradient calculation circuit [11], the desired weights can be obtained immediately.…”

Section: B Hardware-friendly Training Methodsmentioning

confidence: 99%

Design and Implementation of a Flexible Neuromorphic Computing System for Affective Communication via Memristive Circuits

Dong

Lai

et al. 2023

IEEE Commun. Mag.

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Neuromorphic computing is expected to realize fast and energy-efficient artificial neural networks and address the inherent limitations of von Neumann architectures in dedicated communication applications. To realize this vision, we identify the existing challenges in neuromorphic computing and provide a specific solution from the perspectives of device, circuit, and system. At the device level, we fabricate a metal oxide-based memristor with high stability, low power, and good scalability, serving as the fundamental component of neuromorphic computing system. At the circuit level, the basic circuit units and necessary peripheral circuits are designed to realize efficient vector-matrix multiplication and different functions, including nonlinear activation operation, subtraction operation, added operation, etc. At the system level, a flexible neuromorphic computing system with a hardware-friendly training approach is proposed, it can perform affective communication with good trade-off between accuracy and time consumption. This study is expected to achieve the deep integration of nanotechnology, energy-efficient integrated circuits, and neuromorphic computing systems into communication applications.

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“…Recently, various devices, such as resistive random-access memory (RRAM), phase-change random-access memory, ferroelectric tunnel junction, and charge-trapbased flash devices, have been studied as synapses to simultaneously implement nonvolatile and weight change characteristics. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] Among them, RRAM can be made into a 4F 2 crossbar array with a simple structure of two terminals and has the advantage of high operating speed; therefore, many studies have been conducted on RRAMbased synaptic devices. However, an RRAM device that satisfies all desirable characteristics such as low power, multilevel, retention, endurance, and low parameter variation has not been developed yet.…”

Section: Introductionmentioning

confidence: 99%

4‐bit Multilevel Operation in Overshoot Suppressed Al₂O₃/TiO_xResistive Random‐Access Memory Crossbar Array

Kim

Park

Kim

et al. 2022

Advanced Intelligent Systems

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To apply resistive random‐access memory (RRAM) to the neuromorphic system and improve performance, each cell in the array should be able to operate independently by reducing device variation. In addition, it is necessary to lower the operating current of the RRAM cell and enable gradual switching characteristics to mimic the low‐energy operations of biological. In most filamentary RRAMs, however, overshoot current occurs in the forming stage, and the RRAM shows large device variation, high operating current, and abrupt set and reset switching characteristics. Herein, the shortcomings occurring in the forming stage are overcome by introducing and optimizing an overshoot suppression layer. Consequently, the RRAM exhibits gradual switching characteristics both in the set and reset regions, thereby enabling implementation of 4‐bit multilevel operation. In addition, the forming step can be easily performed in a 16 × 16 crossbar array owing to its self‐compliance characteristics without disturbing neighboring cells in the array. The tuning and vector–matrix multiplication (VMM) operations are also experimentally verified in the array. Finally, classification performance with off‐chip training is compared in terms of accuracy and robustness to tuning tolerance depending on the number of bits of the implemented multiconductance levels.

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A fully hardware-based memristive multilayer neural network

Abstract: A fully hardware-based memristive neural network is capable of delivering high computing throughput and power efficiency.

Cited by 54 publications

References 47 publications

A Bio‐Inspired Neuromorphic Sensory System

A Bio‐Inspired Neuromorphic Sensory System

Design and Implementation of a Flexible Neuromorphic Computing System for Affective Communication via Memristive Circuits

4‐bit Multilevel Operation in Overshoot Suppressed Al₂O₃/TiO_xResistive Random‐Access Memory Crossbar Array

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A fully hardware-based memristive multilayer neural network

Abstract: A fully hardware-based memristive neural network is capable of delivering high computing throughput and power efficiency.

Cited by 54 publications

References 47 publications

A Bio‐Inspired Neuromorphic Sensory System

A Bio‐Inspired Neuromorphic Sensory System

Design and Implementation of a Flexible Neuromorphic Computing System for Affective Communication via Memristive Circuits

4‐bit Multilevel Operation in Overshoot Suppressed Al2O3/TiOxResistive Random‐Access Memory Crossbar Array

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Product

Resources

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4‐bit Multilevel Operation in Overshoot Suppressed Al₂O₃/TiO_xResistive Random‐Access Memory Crossbar Array