Toward memristive in-memory computing: principles and applications

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

With the rapid growth of computer science and big data, the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories. Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues, and plentiful applications have been demonstrated and verified. These applications can be broadly categorized into two major types: soft computing that can tolerant uncertain and imprecise results, and hard computing that emphasizes explicit and precise numerical results for each task, leading to different requirements on the computational accuracies and the corresponding hardware solutions. In this review, we conduct a thorough survey of the recent advances of memristive in-memory computing applications, both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms, and the hard computing type that includes scientific computing and digital image processing. At the end of the review, we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.

Graphical Abstract

Related collections

Most cited references 117

Record: found
Abstract: found
Article: not found

Deep learning.

Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

0 comments Cited 8348 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Neural networks and physical systems with emergent collective computational abilities.

J Hopfield (1982)

Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.

0 comments Cited 1712 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Machine learning: Trends, perspectives, and prospects.

M. I. Jordan, T. M. Mitchell (2015)

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

0 comments Cited 973 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Han Bao: d201880662@hust.edu.cn

Bio :

Han Bao

is currently a Ph.D. candidate at Huazhong University of Science and Technology (HUST), China. He received his Bachelor’s degree from HUST in 2018. His research focuses on memristor-based hardware neural network accelerators.

Yi Li: liyi@hust.edu.cn

Bio :

Yi Li

is currently an associate professor at Huazhong University of Science and Technology (HUST), China. He received his Ph.D. degree in Microelectronics from HUST in 2014. His major research interests include memristors and their applications in neuromorphic computing and in-memory computing.

Xiangshui Miao: miaoxs@hust.edu.cn

Bio :

Xiangshui Miao

is a full professor and the Dean of School of Integrated Circuits at Huazhong University of Science and Technology (HUST), China. He received his Ph.D. degree in Microelectronics from HUST in 1996. From 1996 to 1997, he was a researcher in City University of Hong Kong, China. From 1997 to 2007, he was a chief scientist in the Data Storage Institute, A*STAR, Singapore. His research interests include phase change memory, memristors, neuromorphic computing, and in-memory computing. He has published more than 200 papers in renowned journals and conferences, including Science, Nature Communications, Advanced Materials, IEEE EDL, IEEE TED, and IEDM.

Journal

Journal ID (nlm-ta): Front Optoelectron

Journal ID (iso-abbrev): Front Optoelectron

Title: Frontiers of Optoelectronics

Publisher: Higher Education Press (Beijing )

ISSN (Print): 2095-2759

ISSN (Electronic): 2095-2767

Publication date (Electronic): 12 May 2022

Publication date PMC-release: 12 May 2022

Publication date Collection: December 2022

Volume: 15

Issue: 1

Electronic Location Identifier: 23

Affiliations

[1 ]GRID grid.33199.31, ISNI 0000 0004 0368 7223, School of Integrated Circuits, School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, , Huazhong University of Science and Technology, ; Wuhan, 430074 China

[2 ]Hubei Yangtze Memory Laboratories, Wuhan, 430205 China

[3 ]AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China

[4 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, School of Integrated Circuits, , Peking University, ; Beijing, 100871 China

[5 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), , Tsinghua University, ; Beijing, 100084 China

[6 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Frontier Institute of Chip and System, , Fudan University, ; Shanghai, 200433 China

[7 ]GRID grid.41156.37, ISNI 0000 0001 2314 964X, School of Electronic Science and Engineering, and Collaborative Innovation Centre of Advanced Microstructures, , Nanjing University, ; Nanjing, 210093 China

Article

Publisher ID: 25

DOI: 10.1007/s12200-022-00025-4

PMC ID: 9756267

PubMed ID: 36637566

SO-VID: d531852e-c037-4bed-8a12-2ae16acb24ed

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 1 March 2022

Date accepted : 7 March 2022

Custom metadata

Keywords: memristor,in-memory computing,matrix–vector multiplication,machine learning,scientific computing,digital image processing

Data availability:

Keywords: memristor, in-memory computing, matrix–vector multiplication, machine learning, scientific computing, digital image processing

Toward memristive in-memory computing: principles and applications

Read this article at

Abstract

Graphical Abstract

Related collections

Annual Reviews AI, Machine Learning, and Society

Most cited references 117

Deep learning.

Neural networks and physical systems with emergent collective computational abilities.

Machine learning: Trends, perspectives, and prospects.

Author and article information

Contributors

Journal

Affiliations

Article

History

Categories

Custom metadata

Comments

Comment on this article

Similar content 210

Cited by 2

Most referenced authors 2,361