Proceedings Article10.1109/IMW.2015.7150295
Memory Technologies for Neural Networks
Dmitri B. Strukov,Farnood Merrikh-Bayat,Mirko Prezioso,Xinjie Guo,Brian D. Hoskins,Konstantin K. Likharev +5 more
- 17 May 2015
- pp 1-4
22
TL;DR: The CrossNet concept, which was conceived to address major challenges of artificial neural networks, is reviewed, and the recent progress toward CrossNet implementation is discussed, in particular the experimental results for simple networks with crossbar-integrated resistive switching (memristive) metal oxide devices.
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Abstract: Synapses, the most numerous elements of neural networks, are memory devices Similarly to traditional memory applications, device density is one of the most essential metrics for large-scale artificial neural networks This application, however, imposes a number of additional requirements, such as the continuous change of the memory state, so that novel engineering approaches are required In this paper, we briefly review our recent efforts at addressing these needs We start by reviewing the CrossNet concept, which was conceived to address major challenges of artificial neural networks We then discuss the recent progress toward CrossNet implementation, in particular the experimental results for simple networks with crossbar-integrated resistive switching (memristive) metal oxide devices Finally, we review preliminary results on redesigning commercial-grade embedded NOR flash memories to enable individual cell tuning While NOR flash memories are less dense then memristor crossbars, their technology is much more mature and ready for the development of large-scale neural networks
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Citations
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
- 02 Jan 2017
TL;DR: 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.
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Practical Implementation of Memristor-Based Threshold Logic Gates
TL;DR: In this paper, the authors introduce a physical implementation of a memristor-based current-mode threshold logic gates (MCMTLG) circuit and validate its design and operation through multiple experimental setups.
The Cat is Out of the Bag
Laura Gould
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TL;DR: On the twenty-first day of August 1990, our third grandchild is born and George's chromosomes show up in our mailbox, rather pictures of them do, carefully arranged in neat rows to form his karyotype.
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Memristor-based perceptron classifier: increasing complexity and coping with imperfect hardware
Farnood Merrikh Bayat,Mirko Prezioso,Bhaswar Chakrabarti,Irina Kataeva,Dmitri B. Strukov +4 more
- 13 Nov 2017
TL;DR: To deal with larger crossbar arrays, this work has developed a semiautomatic approach to their forming and testing, and compared several memristor training schemes for coping with imperfect behavior of these devices, as well as with variability of analog CMOS neurons.
35
Multi-level memristive memory with resistive networks
Aidana Irmanova,Alex Pappachen James +1 more
- 01 Oct 2017
TL;DR: In this article, the authors presented a new design of discrete state memory cell consisting of sub-cells constructed from a memristor and its resistive network, which can provide blocks of binary or discrete state data storage.
20
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