Memristor for computing: Myth or reality?

Hamdioui, Said; Kvatinsky, Shahar; Cauwenberghs, Gert; Xie, Lei; Wald, Nimrod; Joshi, Siddharth; Elsayed, Hesham Mostafa; Corporaal, Henk; Bertels, Koen

doi:10.23919/date.2017.7927083

Cited by 98 publications

(44 citation statements)

References 69 publications

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“…The simulation approach, on the other hand, require a full simulation per each workload to find actual best and worst cases of power consumption. Memristive devices are under investigation to explore not only their potential for building memories and logic, but also to build radically new computing architectures [36], such as computing-in-memory (CIM).…”

Section: E Experimental Resultsmentioning

confidence: 99%

Memristive devices: Technology, design automation and computing frontiers

Barbareschi

Bosio

Nguyen

et al. 2017

2017 12th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era (DTIS)

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The memristor is an emerging technology which is triggering intense interdisciplinary activity. It has the potential of providing many benefits, such as energy efficiency, density, reconfigurability, nonvolatile memory, novel computational structures and approaches, massive parallelism, etc. These characteristics may lead to deeply revise existing computing and storage paradigms. This paper presents a comprehensive overview of memristor technology and its potential to design a new computational paradigm.

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Section: E Experimental Resultsmentioning

confidence: 99%

Memristive devices: Technology, design automation and computing frontiers

Barbareschi

Bosio

Nguyen

et al. 2017

2017 12th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era (DTIS)

Self Cite

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“…In the past decades, the world has seen a phenomenal increase in computing performance, resulting in smaller, faster, and more energy efficient computers. However, today's computer architectures as well as the CMOS technology used to manufacture them are facing major challenges such as memory wall, power wall, leakage wall, and cost wall [1,2]; these make them economically not attractive for many evolving applications which are extremely demanding, e.g., in terms of MOPs/Watt. Therefore, continuing with delivering sustainable benefits in the foreseeable future requires the exploration of alternative (unconventional) computing architectures that leverage novel post-CMOS device technologies such as memristive devices (e.g., resistive RAM (RRAM), phase change memory (PCM), spin-transfer-torque magnetic RAM (STT-MRAM)).…”

Section: Introductionmentioning

confidence: 99%

Testing Computation-in-Memory Architectures Based on Emerging Memories

Hamdioui

Fieback

Nagarajan

et al. 2019

2019 IEEE International Test Conference (ITC)

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Today's computing architectures and device technologies are becoming incapable of meeting the increasingly stringent demands on energy and performance posed by evolving applications. Therefore, alternative novel post-CMOS computing architectures are being explored. Some of these are Computationin-Memory (CIM) architectures based on memristive devices; they integrate the processing units and the storage in the same physical location (i.e., the memory based on memristive devices). Due to their advanced manufacturing processes, use of new materials, and dual functionality, testing such chips requires specific schemes and therefore special attention. This paper describes the need for testing CIM architectures, proposes a systematic test approach, and shows the strong dependency of the test solutions on the nature of the architecture. All of these will be demonstrated using a design that is designed for computationin-memory bit-wise logical operations.

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“…[2]. This situation known as the "memory wall" encourages the investigation of different processing information paradigms considered as non-Von Neumann (non-VN) architectures [3]. Over the last few years there has been a lot of activity across research groups proposing efficient hybrid "CMOSnanodevice" computing hardware architectures such as Artificial Neural Networks (ANNs) [4].…”

Section: Introductionmentioning

confidence: 99%

An Augmented OxRAM Synapse for Spiking Neural Network (SNN) Circuits

Aziza

Bazzi

Postel-Pellerin

et al. 2019

2019 14th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era (DTIS)

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In this paper, the conductance modulation of OxRAM memristive devices is evaluated based on experimental data to reveal the memristor inherent analog synaptic behavior. Simulation results are presented to validate the use of OxRAMs as synapses at a circuit level in a spiking neural network context. In the proposed approach, the OxRAM synapse is augmented with a shift register associated with current compliance control transistors to provide an efficient monitoring of the OxRAM conductance.

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Memristor for computing: Myth or reality?

Cited by 98 publications

References 69 publications

Memristive devices: Technology, design automation and computing frontiers

Memristive devices: Technology, design automation and computing frontiers

Testing Computation-in-Memory Architectures Based on Emerging Memories

An Augmented OxRAM Synapse for Spiking Neural Network (SNN) Circuits

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