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Resistive Random Access Memory (RRAM) devices hold promise as a key enabler technology for energy-efficient, in-memory, and brain-inspired computing paradigms, with the potential to significantly enhance high-performance computing applications. However, the widespread adoption of RRAM technology in high-performance computing applications is hindered by non-ideal device metrics and various reliability challenges. RRAM devices are reported to exhibit critical device-to-device (D2D) and cycle-to-cycle (C2C) variability. In this paper, we investigate D2D and C2C variabilities of Tantalum Oxide RRAM devices and explore potentiation, depression, and endurance dynamics under varying operation conditions. Our ultimate goal is to address performance and reliability issues associated with the oxide-based RRAM device technology and facilitate its broader implementation in future computing applications.
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Adaptation Strategies for Personalized Gait Neuroprosthetics
Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.
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- Award ID(s):
- 1739800
- PAR ID:
- 10388309
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Frontiers in Neurorobotics
- Volume:
- 15
- ISSN:
- 1662-5218
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fnbot.2021.750519
