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- W3048233773 endingPage "3532" @default.
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- W3048233773 abstract "Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor). Their potential application to Boolean logic computation is then considered in terms of their working mechanism, circuit design and performance metrics. The paper concludes by envisaging the prospects offered by non-volatile devices for future brain-inspired and neuromorphic computation." @default.
- W3048233773 created "2020-08-13" @default.
- W3048233773 creator A5018178973 @default.
- W3048233773 creator A5033062026 @default.
- W3048233773 creator A5050358910 @default.
- W3048233773 creator A5054594954 @default.
- W3048233773 creator A5073216396 @default.
- W3048233773 creator A5082944340 @default.
- W3048233773 date "2020-08-10" @default.
- W3048233773 modified "2023-10-16" @default.
- W3048233773 title "In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory" @default.
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