Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications

Zayer, Fakhreddine; Dghais, Wael; Hamdi, Belgacem

doi:10.1088/1361-6463/ab24a7

Cited by 10 publications

(4 citation statements)

References 132 publications

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“…1(b), with the dimensions in Table II. The typical RESET signal is made by a train of pulses: in each period, there is a "write" pulse of a typical amplitude of about 1 V, followed by a nondestructive "read" pulse, of the typical amplitude of 0.1 V [37], [38]. However, the electrothermal analysis in the literature usually refers to the application of a dc voltage, rather than to the abovementioned train pulses, [16], [36].…”

Section: A Transient Analysis Of a Single Rram Cellmentioning

confidence: 99%

Signal and Thermal Integrity Analysis of 3-D Stacked Resistive Random Access Memories

Zayer

Lahbacha

Melnikov

et al. 2021

IEEE Trans. Electron Devices

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In this article, a 3-D electrothermal numerical model is used to perform the signal and thermal integrity analysis of 3-D stacked Resistive-switching random access memory (RRAM) arrays. Two main issues are found: voltage drop along the interconnects and thermal crosstalk between the memory cells. Possible solutions to these issues are here thoroughly investigated, based either on new biasing schemes or new materials. Especially, conventional nickel bars are replaced by interconnects made by copper (Cu) and carbon nanotubes (CNTs), whose electrical and thermal parameters are here described using physically based models. The analysis is performed on a 5 × 5 × 5 array, under a real case of a RESET switching, which is the worst case scenario from the electrothermal point of view. Simulation results show that the use of CNTs reduces the voltage drop in both word and bitline (BL) interconnects, thermal crosstalk, and the maximum working temperature; hence, it mitigates many of the crucial issues in the roadmap for the large-scale monolithic 3-D RRAM integration.

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Section: A Transient Analysis Of a Single Rram Cellmentioning

confidence: 99%

Signal and Thermal Integrity Analysis of 3-D Stacked Resistive Random Access Memories

Zayer

Lahbacha

Melnikov

et al. 2021

IEEE Trans. Electron Devices

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“…In addition, this "analog" behavior makes the memristor suitable for neuromorphic computing applications. A large number of publications describes the possible use of memristors as artificial synapses [66][67][68][69][70] or even neurons [71][72][73].…”

Section: Memristors and Other Nonmagnetic Neuromorphic Computing Elementsmentioning

confidence: 99%

Magnetic Elements for Neuromorphic Computing

Błachowicz

Ehrmann

2020

Molecules

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Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems.

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“…A memristor contains a thin oxide film sandwiched between two metal electrodes [17] and has the ability to save information with zero leakage current, high endurance, relatively fast write time, and small cell size. Furthermore, the memristor has both storage and computing capabilities, which make it a suitable building block for IMC [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

An Efficient In-Memory Computing Architecture for Image Enhancement in AI Applications

et al. 2022

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Random Spray Retinex (RSR) is an effective image enhancement algorithm due to its effectiveness in improving image quality. However, the algorithm computing complexity and the required hardware resources and memory accesses hampered its deployment in many applications scenarios, for instance in IoT systems with limited hardware resources. With the rise of Artificial Intelligence (AI), the use of image enhancement has become essential to improve the performance for many emerging applications. In this paper, we propose the use of the RSR as a pre-processing filter before the task of semantic segmentation of low-quality urban road scenes. Using the publicly available Cityscapes dataset, we compare the performance of a pre-trained deep semantic segmentation network on dark noisy images and on RSR pre-processed images. Our findings confirm the effectiveness of RSR in improving segmentation accuracy. In addition, to address the computation complexity and suitability to edge devices, we propose a novel efficient implementation of the RSR using resistive random access memory (RRAM) technology. The architecture provides highly parallel analog in-memory computing (IMC) capabilities. A detailed, efficient and low latency implementation of the RSR using RRAM-CMOS technology is described. The design is verified using SPICE simulations with measured data from fabricated RRAM and 65nm CMOS technologies. The approach provided here represents an important step towards low-complexity and realtime hardware-friendly architecture and design for Retinex algorithms for edge devices.

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Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications

Cited by 10 publications

References 132 publications

Signal and Thermal Integrity Analysis of 3-D Stacked Resistive Random Access Memories

Signal and Thermal Integrity Analysis of 3-D Stacked Resistive Random Access Memories

Magnetic Elements for Neuromorphic Computing

An Efficient In-Memory Computing Architecture for Image Enhancement in AI Applications

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