Research Progress on Memristor: From Synapses to Computing Systems

Yang, Xiaoxuan; Taylor, Brady; Wu, Ailong; Chen, Yiran; Chua, Leon O.

doi:10.1109/tcsi.2022.3159153

Cited by 80 publications

(36 citation statements)

References 124 publications

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“…It is obvious that when no voltage applied to either of two terminals, the boundary between the two titanium dioxide layers remains unchanged. This allows the memristor to maintain its previous state [8][9][10][11]. After simulation of the basic modules in SPICE, the memristor was implemented using VTEAM-based Verilog-A codes.…”

Section: Brief Review Of Memristormentioning

confidence: 99%

“…Due to the special and unique characteristics of memristor as well as the compatibility of the manufacturing technologies, a lot of research is being done on mixed CMOS-Memristor circuits. It is now wellknown that memristor is one of the leading and prominent emerging devices and technologies for advancing hardware security and computing systems [9,10]. The novel nanoelectronics devices such as memristor can be harnessed for novel computing systems that are naturally immune to many existing conventional cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

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Design and Evaluation of Memristor-Based Piccolo-80 Lightweight Encryption Algorithm for Future IoT Applications

Masoumi

2022

Preprint

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Piccolo is an ultra-lightweight encryption algorithm optimized for very resource-constrained platforms. However, lightweight algorithms are vulnerable to side-channel attacks, specifically Differential Power Analysis (DPA). In this study, we first demonstrate the vulnerability of this algorithm against DPA. Then, a new masking scheme to protect the algorithm against DPA is proposed. In addition, the same implementation in a hybrid CMOS-Nano system is presented and the results are compared in terms of attack tolerance, area, and energy consumption. Power analysis mounted on unmasked Piccolo was successful while the attack on the masked version of both CMOS and CMOS-Nano implementations of the algorithm led to unsuccessful results and no key was recovered. More importantly, while maintaining reliable performance, the hybrid CMOS-Nano circuit was more efficient than the pure CMOS circuit in terms of energy consumption and area. This is a very important issue for future IoT devices and applications where energy consumption and security are the primary concerns.

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Section: Brief Review Of Memristormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of Memristor-Based Piccolo-80 Lightweight Encryption Algorithm for Future IoT Applications

Masoumi

2022

Preprint

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“…According to the amount and direction of the electric charges q(t) travelling across the memristor, the width of the doped area w(t) changes between zero and a memristor length of D. As a result, introducing a bias to the memristor causes current to flow, which affects the value of w(t). As a result, the virtual boundary between the doped and undoped areas moves, which changes the memristor's total resistance, as shown in Equation (2) [10].…”

Section: ░ 2 Memristormentioning

confidence: 99%

Performance Evaluation of Low Power Hybrid Combinational Circuits using Memristor

Garg

Wairya²

2022

IJEER

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Recently, extending the use of memristor technology from memory to computing has received a lot of attention. Memristor-based logic design is a new concept that aims to make computing systems more efficient. Several logic families have emerged, each with its own set of characteristics. In this paper, CMOS-based hybrid memristor-based combinational circuits are designed. Many computational devices require combinational circuits. All of the proposed designs were analysed for power, latency, and transistor count. Cadence Virtuoso is used for simulation of circuits. In this study, we used the VTEAM model to describe the simulated memristor because it is easy to understand and gives accurate results.

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“…While exploring the advanced BIC paradigms, some reviews have been carried out in terms of applied materials (Ko et al, 2020;Liu et al, 2020) or devices (Kwon et al, 2022;Meier and Selbach, 2022) or neuromorphic computing algorithms (Kumar et al, 2022;Yang et al, 2022). The two-dimensional (2D) materials were reviewed to apply for the next-generation computing technologies (Liu et al, 2020), which is focusing on a broader application scope in neuromorphic computing, matrix computing, and logic computing.…”

Section: Introductionmentioning

confidence: 99%

“…The 2D memristive devices were reviewed for the applications of neuromorphic computing, and the fabrication and characterization of neuromorphic memristors were primarily discussed (Kwon et al, 2022). Moreover, the research progress on memristors especially as artificial synapses to neuromorphic systems is reviewed by Yang et al (2022).…”

Section: Introductionmentioning

confidence: 99%

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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Research Progress on Memristor: From Synapses to Computing Systems

Cited by 80 publications

References 124 publications

Design and Evaluation of Memristor-Based Piccolo-80 Lightweight Encryption Algorithm for Future IoT Applications

Design and Evaluation of Memristor-Based Piccolo-80 Lightweight Encryption Algorithm for Future IoT Applications

Performance Evaluation of Low Power Hybrid Combinational Circuits using Memristor

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

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