Neuromorphic computing using non-volatile memory
Geoffrey W. Burr,Robert M. Shelby,Abu Sebastian,Sangbum Kim,Seyoung Kim,Severin Sidler,Kumar Virwani,Masatoshi Ishii,Pritish Narayanan,Alessandro Fumarola,Lucas L. Sanches,Irem Boybat,Manuel Le Gallo,Kibong Moon,Jiyoo Woo,Hyunsang Hwang,Yusuf Leblebici +16 more
- Vol. 2, Iss: 1, pp 89-124
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TLDR
The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.Abstract:
Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first revie...read more
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Principles of Neural Science
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Journal ArticleDOI
Fully hardware-implemented memristor convolutional neural network
Peng Yao,Huaqiang Wu,Bin Gao,Jianshi Tang,Qingtian Zhang,Wenqiang Zhang,Jianhua Yang,He Qian +7 more
TL;DR: The fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs and an effective hybrid-training method to adapt to device imperfections and improve the overall system performance are proposed.
Journal ArticleDOI
Memristive crossbar arrays for brain-inspired computing
Qiangfei Xia,Jianhua Yang +1 more
TL;DR: The challenges in the integration and use in computation of large-scale memristive neural networks are discussed, both as accelerators for deep learning and as building blocks for spiking neural networks.
Journal ArticleDOI
Towards spike-based machine intelligence with neuromorphic computing.
TL;DR: An overview of the developments in neuromorphic computing for both algorithms and hardware is provided and the fundamentals of learning and hardware frameworks are highlighted, with emphasis on algorithm–hardware codesign.
Journal ArticleDOI
Memory devices and applications for in-memory computing
TL;DR: This Review provides an overview of memory devices and the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.
References
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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