Fully hardware-implemented memristor convolutional neural network

Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks 1 – 4 . However, convolutional neural networks (CNNs)—one of the most important models for image recognition 5 —have not yet been fully hardware-implemented using memristor cross...

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Published in	Nature (London) Vol. 577; no. 7792; pp. 641 - 646
Main Authors	Yao, Peng, Wu, Huaqiang, Gao, Bin, Tang, Jianshi, Zhang, Qingtian, Zhang, Wenqiang, Yang, J. Joshua, Qian, He
Format	Journal Article
Language	English
Published	London Nature Publishing Group UK 30.01.2020 Nature Publishing Group
Subjects	639/166/987 639/925/927/1007 Accuracy Arrays Artificial neural networks Design and construction Edge computing Efficiency Energy efficiency Fabrication Hardware Humanities and Social Sciences Kernels Memristors Methods multidisciplinary Neural networks Object recognition Science Science (multidisciplinary) Software Training Transistors China
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Abstract	Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks 1 – 4 . However, convolutional neural networks (CNNs)—one of the most important models for image recognition 5 —have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices 6 – 9 . Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST 10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. A fully hardware-based memristor convolutional neural network using a hybrid training method achieves an energy efficiency more than two orders of magnitude greater than that of graphics-processing units.
AbstractList	Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks.sup.1-4. However, convolutional neural networks (CNNs)--one of the most important models for image recognition.sup.5--have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices.sup.6-9. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST.sup.10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks.sup.1-4. However, convolutional neural networks (CNNs)--one of the most important models for image recognition.sup.5--have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices.sup.6-9. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST.sup.10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. A fully hardware-based memristor convolutional neural network using a hybrid training method achieves an energy efficiency more than two orders of magnitude greater than that of graphics-processing units. Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks1-4. However, convolutional neural networks (CNNs)-one of the most important models for image recognition5-have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices6-9. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing.Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks1-4. However, convolutional neural networks (CNNs)-one of the most important models for image recognition5-have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices6-9. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. Memristor-enabled neuromorphic computing systems provide a fast and energyefficient approach to training neural networks1-4. However, convolutional neural networks (CNNs)-one of the most important models for image recognition5-have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices6-9. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybridtraining method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks . However, convolutional neural networks (CNNs)-one of the most important models for image recognition -have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices . Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks 1 – 4 . However, convolutional neural networks (CNNs)—one of the most important models for image recognition 5 —have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices 6 – 9 . Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST 10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. A fully hardware-based memristor convolutional neural network using a hybrid training method achieves an energy efficiency more than two orders of magnitude greater than that of graphics-processing units.
Audience	Academic
Author	Qian, He Zhang, Wenqiang Tang, Jianshi Yao, Peng Gao, Bin Zhang, Qingtian Wu, Huaqiang Yang, J. Joshua
Author_xml	– sequence: 1 givenname: Peng surname: Yao fullname: Yao, Peng organization: Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University – sequence: 2 givenname: Huaqiang surname: Wu fullname: Wu, Huaqiang email: wuhq@tsinghua.edu.cn organization: Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University – sequence: 3 givenname: Bin surname: Gao fullname: Gao, Bin organization: Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University – sequence: 4 givenname: Jianshi surname: Tang fullname: Tang, Jianshi organization: Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University – sequence: 5 givenname: Qingtian surname: Zhang fullname: Zhang, Qingtian organization: Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University – sequence: 6 givenname: Wenqiang surname: Zhang fullname: Zhang, Wenqiang organization: Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University – sequence: 7 givenname: J. Joshua surname: Yang fullname: Yang, J. Joshua organization: Department of Electrical and Computer Engineering, University of Massachusetts – sequence: 8 givenname: He surname: Qian fullname: Qian, He organization: Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31996818$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer Nature Limited 2020 COPYRIGHT 2020 Nature Publishing Group Copyright Nature Publishing Group Jan 30, 2020
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Nature Limited 2020 – notice: COPYRIGHT 2020 Nature Publishing Group – notice: Copyright Nature Publishing Group Jan 30, 2020
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Snippet	Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks 1 – 4 . However, convolutional neural... Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks . However, convolutional neural... Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks.sup.1-4. However, convolutional... Memristor-enabled neuromorphic computing systems provide a fast and energyefficient approach to training neural networks1-4. However, convolutional neural... Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks1-4. However, convolutional neural...
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SubjectTerms	639/166/987 639/925/927/1007 Accuracy Arrays Artificial neural networks Design and construction Edge computing Efficiency Energy efficiency Fabrication Hardware Humanities and Social Sciences Kernels Memristors Methods multidisciplinary Neural networks Object recognition Science Science (multidisciplinary) Software Training Transistors
Title	Fully hardware-implemented memristor convolutional neural network
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