Accuracy Investigation of a Neuromorphic Machine Learning System Due to Electromagnetic Noises Using PEEC Model

Lee, Wooryong; Kim, Jingook

doi:10.1109/tcpmt.2019.2917910

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…Using DNN training and real measurements, the magnitude of the impact of interconnection parasitic of memristor arrays problems (e.g., IR drop, crosstalk or ringing) on signal integrity was determined. Similar research works have been carried out (Chen et al, 2015;Lee and Kim, 2019). In addition, research works on robotic controls (Abbeel et al, 2010) and drug discovery (Lavecchia, 2015) to image classification (Krizhevsky et al, 2012;Liu et al, 2016;Adam et al, 2018;Tang et al, 2020;Liu and Zeng, 2022) and language translation (Wu et al, 2016) have also been performed.…”

Section: Discrete Data Structurementioning

confidence: 83%

Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

Wang

Kvatinsky²,

Schmidt³

et al. 2022

Front. Electron. Mater

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Biologically-inspired neuromorphic computing paradigms are computational platforms that imitate synaptic and neuronal activities in the human brain to process big data flows in an efficient and cognitive manner. In the past decades, neuromorphic computing has been widely investigated in various application fields such as language translation, image recognition, modeling of phase, and speech recognition, especially in neural networks (NNs) by utilizing emerging nanotechnologies; due to their inherent miniaturization with low power cost, they can alleviate the technical barriers of neuromorphic computing by exploiting traditional silicon technology in practical applications. In this work, we review recent advances in the development of brain-inspired computing (BIC) systems with respect to the perspective of a system designer, from the device technology level and circuit level up to the architecture and system levels. In particular, we sort out the NN architecture determined by the data structures centered on big data flows in application scenarios. Finally, the interactions between the system level with the architecture level and circuit/device level are discussed. Consequently, this review can serve the future development and opportunities of the BIC system design.

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Section: Discrete Data Structurementioning

confidence: 83%

Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

Wang

Kvatinsky²,

Schmidt³

et al. 2022

Front. Electron. Mater

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“…However, there are few research on frequency domain S‐parameters extraction. The authors of [11] utilized parasitic parameter extraction without considering time retardation effects, and then used SPICE simulation tool to analyze S‐parameter, which results in low efficiency and coherency. In fact, S ‐parameter is a critical frequency domain characteristic in electronic systems for assessing electromagnetic performance of memristor‐based crossbar array circuits and facilitating performance and stability optimization of these circuits.…”

Section: Introductionmentioning

confidence: 99%

“…Several previous studies applied time domain signal integrity modelling on memristor-based crossbar arrays [9][10][11][12][13]. However, there are few research on frequency domain S-parameters extraction.…”

mentioning

confidence: 99%

S‐parameter extraction for electromagnetic modelling of memristor‐based crossbar array circuits

Yu,

Ma,

Tao

et al. 2024

Electronics Letters

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This article presents an efficient S‐parameter extraction method for memristor‐based crossbar array circuits. The proposed approach involves converting the array into an equivalent circuit and analytically solving the circuit to obtain the S‐parameters. Memristor‐based crossbar array circuit simulations and corresponding experimental measurements are conducted to verify the high accuracy and low computation complexity of the established method. These numerical results indicate that the authors’ S‐parameter extraction method has the potential for evaluating signal integrity performance in neuromorphic chips, thus aiding their design and optimization for diverse applications.

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“…It is worth noting that with the advancement of electron science and technology, the number of radio-using devices is increasing, the space is filled with various intentional and unintentional electromagnetic wave signals that ramify and overlap in the time domain, frequency domain, and space domain [ 23 ], which are serious threats to the reliability of highly integrated and miniaturized memristive systems. Previous studies have focused on the reliability issues of memristors, including endurance, retention deterioration, read/write noise [ 24 ], and signal integrity issues [ 25 , 26 , 27 , 28 ], which may be caused by manufacturing processes, read/write operations, structure, or size of the memristors. However, the interference effects of external EMI on memristors have rarely been considered.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study

Ma¹,

Man²,

Zhang

et al. 2022

Sensors

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As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects on memristors are still unclear. This paper selects continuous waves (CWs) as EMI signals. It provides a deeper insight into the interference effect of CWs on the memristor driven by a sinusoidal excitation voltage, as well as a method for investigating the EMI effect of memristors. The optimal memristor model is obtained by the exhaustive traversing of the possible model parameters, and the interference effect of CWs on memristors is quantified based on this model and the proposed evaluation metrics. Simulation results indicate that CW interference may affect the switching time, dynamic range, nonlinearity, symmetry, time to the boundary, and variation of memristance. The specific interference effect depends on the operating mode of the memristor, the amplitude, and the frequency of the CW. This research provides a foundation for evaluating EMI effects and designing electromagnetic protection for memristive neuromorphic systems.

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Accuracy Investigation of a Neuromorphic Machine Learning System Due to Electromagnetic Noises Using PEEC Model

Cited by 14 publications

References 41 publications

Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

S‐parameter extraction for electromagnetic modelling of memristor‐based crossbar array circuits

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study

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