In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory

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 conv...

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Vydáno v:Materials Ročník 13; číslo 16; s. 3532
Hlavní autoři: Ou, Qiao-Feng, Xiong, Bang-Shu, Yu, Lei, Wen, Jing, Wang, Lei, Tong, Yi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 10.08.2020
MDPI
Témata:
Algorithms
Artificial intelligence
Boolean algebra
Brain
Central processing units
Circuit design
CMOS
Computation
CPUs
Data storage
Electric fields
Energy consumption
Ferroelectricity
Graphene
Logic
Machine learning
Neuromorphic computing
Performance measurement
Random access memory
Review
Transistors
ISSN:1996-1944, 1996-1944
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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.
AbstractList 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.
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.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.
Author Wang, Lei
Tong, Yi
Xiong, Bang-Shu
Ou, Qiao-Feng
Yu, Lei
Wen, Jing
AuthorAffiliation 2 College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
1 School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China; Ou.Qiaofeng@nchu.edu.cn (Q.-F.O.); xiongbs@nchu.edu.cn (B.-S.X.); yulei@nchu.edu.cn (L.Y.); wenj@nchu.edu.cn (J.W.)
AuthorAffiliation_xml – name: 1 School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China; Ou.Qiaofeng@nchu.edu.cn (Q.-F.O.); xiongbs@nchu.edu.cn (B.-S.X.); yulei@nchu.edu.cn (L.Y.); wenj@nchu.edu.cn (J.W.)
– name: 2 College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Author_xml – sequence: 1
  givenname: Qiao-Feng
  surname: Ou
  fullname: Ou, Qiao-Feng
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Snippet Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in...
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SubjectTerms Algorithms
Artificial intelligence
Boolean algebra
Brain
Central processing units
Circuit design
CMOS
Computation
CPUs
Data storage
Electric fields
Energy consumption
Ferroelectricity
Graphene
Logic
Machine learning
Neuromorphic computing
Performance measurement
Random access memory
Review
Transistors
Title In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory
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