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Open AccessJournal ArticleDOI

Write Termination circuits for RRAM : A Holistic Approach From Technology to Application Considerations

TLDR
This work proposes an industrially-ready WT circuit that was simulated with a RRAM model calibrated on real measurements, and performs extensive CMOS and RRAM variability simulations to extract the actual performances of the proposed WT circuit.
Abstract
While Resistive Random Access Memories (RRAM) are perceived nowadays as a promising solution for the future of computing, these technologies suffer from intrinsic variability regarding programming voltage, switching speed and achieved resistance values. Write Termination (WT) circuits are a potential solution to solve these issues. However, previously reported WT circuits do not demonstrate sufficient reliability. In this work, we propose an industrially-ready WT circuit that was simulated with a RRAM model calibrated on real measurements. We perform extensive CMOS and RRAM variability simulations to extract the actual performances of the proposed WT circuit. Finally, we simulate the effects of the proposed WT circuit with memory traces extracted from real Edge-level data-intensive applications. Overall, we demonstrate 2× to 40× of energy gains at bit level. Moreover, we show from 1.9× to 16.2× energy gains with real applications running depending on the application memory access pattern thanks to the proposed WT circuit.

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Citations
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Fundamental variability limits of filament-based RRAM

TL;DR: In this paper, the variability limits of filament-based resistive RAM arrays in the full resistance range are identified, and extensive characterizations of multi-kbits RRAM arrays during Forming, Set, Reset and cycling operations are presented allowing the quantification of the intrinsic variability factors.
Proceedings ArticleDOI

Density Enhancement of RRAMs using a RESET Write Termination for MLC Operation

TL;DR: In this article, a self-adaptive write termination circuit is proposed to control the RRAM RESET current, which is defined as the minimal current allowed by the termination circuit in the RESET direction.
Proceedings ArticleDOI

On the Development of MCU-based ad hoc HW Interface Circuitry for Memristor Characterization

TL;DR: Early results from the experience on the design and development of an instrumentation printed circuit board (PCB), designed to provide an ad hoc low-cost solution for measurements on memristor, are presented.
Proceedings ArticleDOI

Embedded measurement of the SET switching time of RRAM memory cells

TL;DR: In this paper , an embedded measurement circuit dedicated to the extraction of the SET switching time of RRAM memory cells is presented, which is based on a hybrid 130nm technology with HfO2 BEoL RRAMs, with emphasis on the write termination mechanism and the switching time acquisition thanks to a Time-to-Digital Converter (TDC) shift and capture mechanism.
Journal ArticleDOI

Efficient Identification of Critical Faults in Memristor-Based Inferencing Accelerators

TL;DR: In this article , a misclassification-driven training (MDT) algorithm was proposed to identify critical faults in the crossbar of a DNN model, which can rapidly and accurately identify a large number of FCFs.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Metal–Oxide RRAM

TL;DR: The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide resistive switching random access memory (RRAM) are discussed, with a focus on the use of RRAM for nonvolatile memory application.
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NVSim: A Circuit-Level Performance, Energy, and Area Model for Emerging Nonvolatile Memory

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Journal ArticleDOI

Deep Learning With Edge Computing: A Review

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Benchmark Analysis of Representative Deep Neural Network Architectures

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