Reliability characterization of a commercial TaO&lt;inf&gt;x&lt;/inf&gt;-based ReRAM

Yang-Scharlotta, Jean; Fazio, Megan E.; Amrbar, Mehran; White, Mark H.; Sheldon, Douglas

doi:10.1109/iirw.2014.7049528

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Indeed, the works presented so far are based either on simulations or on real device data, or on memristive chips interfaced with some standard digital hardware. Despite integration of CMOS technology has been demonstrated for non-volatile resistive switching devices already at a commercial level (Yang-Scharlotta et al, 2014 ; Hayakawa et al, 2015 ), the design of co-integrated memristive-based neuromorphic processors is still under development. We envisage a three-phase process to achieve a fully integrated system.…”

Section: Memristive Devices and Computingmentioning

confidence: 99%

Adaptive Extreme Edge Computing for Wearable Devices

et al. 2021

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Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

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Section: Memristive Devices and Computingmentioning

confidence: 99%

Adaptive Extreme Edge Computing for Wearable Devices

et al. 2021

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“…The implementation of memristor devices remains under research and development, considering various materials and arrangements like titanium dioxide [111], spintronics [112], or carbon nanotubes [113], with most approaches remaining compatible with CMOS fabrication. Memristive systems in the broader sense, like resistive random-access memories (ReRAMs) or even phase-change memories (PCMs), 3 are progressing towards commercial application [118,119]. Aside from memory, memristors are also interesting for in-memory computing, neuromorphic computing, and reconfigurable logic [115,120,121].…”

Section: Memristorsmentioning

confidence: 99%

Hardware Security For and Beyond CMOS Technology

Knechtel¹

2020

Proceedings of the 2020 International Symposium on Physical Design

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As with most aspects of electronic systems and integrated circuits, hardware security has traditionally evolved around the dominant CMOS technology. However, with the rise of various emerging technologies, whose main purpose is to overcome the fundamental limitations for scaling and power consumption of CMOS technology, unique opportunities arise also to advance the notion of hardware security. In this paper, I first provide an overview on hardware security in general. Next, I review selected emerging technologies, namely (i) spintronics, (ii) memristors, (iii) carbon nanotubes and related transistors, (iv) nanowires and related transistors, and (v) 3D and 2.5D integration. I then discuss their application to advance hardware security and also outline related challenges. CCS CONCEPTS• Security and privacy → Security in hardware; • Hardware → Emerging technologies.

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“…Indeed, the works presented so far are based either on simulations or on real device data, or on memristive chips interfaced with some standard digital hardware. Despite integration of CMOS technology has been demonstrated for non-volatile resistive switching devices already at a commercial level ( 261,262 ), the design of co-integrated memristive-based neuromorphic processors is still under development. We envisage a three-phase process to achieve a fully integrated system.…”

Section: Co-integration Of Hybrid Cmos-memristive Neuromorphic Systemsmentioning

confidence: 99%

Adaptive Extreme Edge Computing for Wearable Devices

Covi¹,

Donati²,

Heidari³

et al. 2020

Preprint

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Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

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Reliability characterization of a commercial TaO<inf>x</inf>-based ReRAM

Cited by 5 publications

References 14 publications

Adaptive Extreme Edge Computing for Wearable Devices

Adaptive Extreme Edge Computing for Wearable Devices

Hardware Security For and Beyond CMOS Technology

Adaptive Extreme Edge Computing for Wearable Devices

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