Fault Tolerance of Memristor-Based Perceptron Network for Neural Interface

Shchanikov, S.A.; Bordanov, Ilya; Zuev, A. D.; Danilin, S.N.; Королев, Д. С.; Belov, A. I.; Mikhaylov, A. N.

doi:10.1007/s12668-020-00807-0

Cited by 11 publications

(2 citation statements)

References 33 publications

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“…In addition to managing operating modes, the tasks of MemriSim include processing measurement results and evaluating cross-bar performance metrics [21], such as accuracy [22,23], fault tolerance [24,25] and reliability. The practical application of this system is presented in [26].…”

Section: Memristive Ai Acceleratormentioning

confidence: 99%

AI Acceleration Enabled by Nanoelectronic Memristive Devices

Bordanov

Zuev

Danilin

et al. 2022

J. Phys.: Conf. Ser.

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Here we present an analysis of the current state in the field of development of hardware accelerators of artificial intelligence (AI). Despite the fairly good progress made over the past decades, this area is experiencing a number of significant difficulties in its development. The solution to this problem lies in the application of new approaches to the organization of computing, in particular, computing in memory enabled by nanoelectronic memristive devices. We provide an overview of state-of-art systems, as well as our own version of the experimental concept of AI accelerators based on metal-oxide memristive devices and the massively parallel architecture for information processing.

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Section: Memristive Ai Acceleratormentioning

confidence: 99%

AI Acceleration Enabled by Nanoelectronic Memristive Devices

Bordanov

Zuev

Danilin

et al. 2022

J. Phys.: Conf. Ser.

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“…Among others, Mehonic et al [17] reported hand-written digit recognition with up to 97% accuracy ratio using an RRAM-based ANN. However, to our best knowledge, the overall assessment of the RRAM MAC operation in a simulation environment is not yet fully performed and includes several simplifications due to the limits of the underlying hardware model [18], [19], [20], [21], [22]. Recently, Bengel et al [23] experimentally analyzed the impact of binary RRAM nonidealities in VMM operations highlighting the fact that the low resistive state (LRS) variability plays a major role compared to the high resistive state (HRS) variability when performing MAC operations.…”

mentioning

confidence: 99%

Experimental Assessment of Multilevel RRAM-Based Vector-Matrix Multiplication Operations for In-Memory Computing

Quesada

Mahadevaiah

Rizzi

et al. 2023

IEEE Trans. Electron Devices

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Resistive random access memory (RRAM)based hardware accelerators are playing an important role in the implementation of in-memory computing (IMC) systems for artificial intelligence applications. The latter heavily rely on vector-matrix multiplication (VMM) operations that can be efficiently boosted by RRAM devices. However, the stochastic nature of the RRAM technology is still challenging real hardware implementations. To study the accuracy degradation of consecutive VMM operations, in this work we programed two RRAM subarrays composed of 8 × 8 one-transistor-one-resistor (1T1R) cells following two different distributions of conductive levels. We analyze their robustness against 1000 identical consecutive VMM operations and monitor the inherent devices' nonidealities along the test. We finally quantize the accuracy loss of the operations in the digital domain and consider the trade-offs between linearly distributing the resistive states of the RRAM cells and their robustness against nonidealities for future implementation of IMC hardware systems.

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Elements of System Analysis and Factographic Information System for Telemedicine

Kulik,

Kondakov

2024

Studies in Computational Intelligence

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Fault Tolerance of Memristor-Based Perceptron Network for Neural Interface

Cited by 11 publications

References 33 publications

AI Acceleration Enabled by Nanoelectronic Memristive Devices

AI Acceleration Enabled by Nanoelectronic Memristive Devices

Experimental Assessment of Multilevel RRAM-Based Vector-Matrix Multiplication Operations for In-Memory Computing

Elements of System Analysis and Factographic Information System for Telemedicine

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