Emerging resistive memories for low power embedded applications and neuromorphic systems

DeSalvo, B.; Vianello, E.; Thomas, Olivier; Clermidy, Fabien; Bichler, Olivier; Gamrat, Christian; Perniola, L.

doi:10.1109/iscas.2015.7169340

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…Memristive materials are generally categorized into anion‐migration‐based and cation‐migration‐based resistance switching materials . Numerous memristive materials, such as binary transition metal oxides, and multinary complex oxides, have been extensively studied and can be placed into the anion‐migration‐based memristive materials category. Cation‐migration‐based memristors include an active electrode‐containing diffusive metal (Ag, Cu, or their alloys) with a solid electrolyte, such as chalcogenides, amorphous silicon or other ionic conductors .…”

Section: Improvements In the Materials Aspectsmentioning

confidence: 99%

“…Numerous memristive materials, such as binary transition metal oxides, and multinary complex oxides, have been extensively studied and can be placed into the anion‐migration‐based memristive materials category. Cation‐migration‐based memristors include an active electrode‐containing diffusive metal (Ag, Cu, or their alloys) with a solid electrolyte, such as chalcogenides, amorphous silicon or other ionic conductors . Despite the prevalence of the ionic resistive switching system described above, the electronic resistive switching system can also be included in memristive materials .…”

Section: Improvements In the Materials Aspectsmentioning

confidence: 99%

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Memristors for Energy‐Efficient New Computing Paradigms

Jeong

Kim

et al. 2016

Adv Elect Materials

313

203

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In this Review, memristors are examined from the frameworks of both von Neumann and neuromorphic computing architectures. For the former, a new logic computational process based on the material implication is discussed. It consists of several memristors which play roles of combined logic processor and memory, called stateful logic circuit. In this circuit confi guration, the logic process fl ows primarily along a time dimension, whereas in current von Neumann computers it occurs along a spatial dimension. In the stateful logic computation scheme, the energy required for the data transfer between the logic and memory chips can be saved. The non-volatile memory in this circuit also saves the energy required for the data refresh. Neuromorphic (cognitive) computing refers to a computing paradigm that mimics the human brain. Currently, the neuromorphic or cognitive computing mainly relies on the software emulation of several brain functionalities, such as image and voice recognition utilizing the recently highlighted deep learning algorithm. However, the human brain typically consumes ≈10-20 Watts for selected "human-like" tasks, which can be currently mimicked by a supercomputer with power consumption of several tens of kilo-to megawatts. Therefore, hardware implementation of such brain functionality must be eventually sought for power-effi cient computation. Several fundamental ideas for utilizing the memristors and their recent progresses in these regards are reviewed. Finally, material and processing issues are dealt with, which is followed by the conclusion and outlook of the fi eld. These technical improvements will substantially decrease the energy consumption for futuristic information technology.

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Section: Improvements In the Materials Aspectsmentioning

confidence: 99%

Section: Improvements In the Materials Aspectsmentioning

confidence: 99%

Memristors for Energy‐Efficient New Computing Paradigms

Jeong

Kim

et al. 2016

Adv Elect Materials

313

203

View full text Add to dashboard Cite

show abstract

“…2a, V SET value needed to switch to LRS state is equal to 0.57 V, while the V RESET value required to switch back to HRS state is equal to -0.7 V. Note that the log curve is the classical representation of the OxRAM I-V hysteresis as it amplifies low current values. Regarding reliability, as demonstrated in [20], SET/RESET endurance was evaluated up to 10 8 cycles showing that the oxide-based technology is in agreement with SRAM operation.…”

Section: T1r Structurementioning

confidence: 83%

Non-volatile SRAM memory cells based on ReRAM technology

et al. 2020

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Static Random-Access Memories (SRAMs) are very common in today's chip industry due to their speed and power consumption but are classified as volatile memories. Non-volatile SRAMs (nvSRAMs) combine SRAM features with nonvolatility. This combination has the advantage to retain data after power off or in the case of power failure, enabling energy-efficient and reliable systems under frequent power-off conditions. In this work, several nvSRAMs architectures based on Oxide Random-Access Memory (OxRAM) technology are presented and compared. OxRAMs are non-volatile memories considered as a subset of Resistive RAM (ReRAM) technology.

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“…Resistive RAMs (ReRAMs) [57][58][59][60][61][62][63], which store the information as the variation of resistivity of a thin oxide film. A current is injected in the oxide to change its structure and to modify its resistance value.…”

Section: Non-volatile Memoriesmentioning

confidence: 99%

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

et al. 2019

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In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

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Emerging resistive memories for low power embedded applications and neuromorphic systems

Cited by 11 publications

References 16 publications

Memristors for Energy‐Efficient New Computing Paradigms

Memristors for Energy‐Efficient New Computing Paradigms

Non-volatile SRAM memory cells based on ReRAM technology

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

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