The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Spintronic Nanodevices for Bioinspired Computing
Open accessAbstract
Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultra-high-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, non-volatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal-oxide-semiconductor (CMOS) bioinspired hardware.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Memristive devices for computing
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
Structural and Functional Brain Networks: From Connections to Cognition
Park, H.-J., & Friston, K. (2013) · Science · doi:10.1126/science.1238411 · Cited by 2,106
Neural correlations, population coding and computation
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743
The role of phase synchronization in memory processes
Fell, J., & Axmacher, N. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn2979 · Cited by 1,620
Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014) · doi:10.1017/cbo9781107447615 · Cited by 1,515
The cost of cortical computation.
Lennie, P. (2003) · Current Biology · doi:10.1016/s0960-9822(03)00135-0 · Cited by 1,134
Chaos in random neural networks
Sompolinsky, H., Crisanti, A., & Sommers, H. J. (1988) · Physical Review Letters · doi:10.1103/physrevlett.61.259 · Cited by 1,061
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology
McDonnell, M. D., & Abbott, D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000348 · Cited by 774
Pattern classification by memristive crossbar circuits using ex situ and in situ training
Alibart, F., Zamanidoost, E., & Strukov, D. B. (2013) · Nature Communications · doi:10.1038/ncomms3072 · Cited by 642
Neural circuits as computational dynamical systems
Sussillo, D. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.008 · Cited by 247
Stochastic resonance in a single neuron model: Theory and analog simulation
Bulsara, A., Jacobs, E. W., Zhou, T., Moss, F., & Kiss, L. (1991) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(05)80396-0 · Cited by 234
Electronic system with memristive synapses for pattern recognition
Park, S., Chu, M., Kim, J., Noh, J., Jeon, M., Hun Lee, B., Hwang, H., Lee, B., & Lee, B.-G. (2015) · Scientific Reports · doi:10.1038/srep10123 · Cited by 181
Synchronous Non-Volatile Logic Gate Design Based on Resistive Switching Memories
Zhao, W., Moreau, M., Deng, E., Zhang, Y., Portal, J.-M., Klein, J.-O., Bocquet, M., Aziza, H., Deleruyelle, D., Muller, C., et al. (2014) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2013.2278332 · Cited by 120
Self-citationFischetti, M. (2011) · Scientific American · doi:10.1038/scientificamerican1111-104 · Cited by 20
Cited by
Neuromorphic computing with nanoscale spintronic oscillators
Torrejon, J., Riou, M., Araujo, F. A., Tsunegi, S., Khalsa, G., Querlioz, D., Bortolotti, P., Cros, V., Yakushiji, K., Fukushima, A., et al. (2017) · Nature · doi:10.1038/nature23011 · Cited by 1,374
Self-citationA comprehensive review on emerging artificial neuromorphic devices
Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 758
Perspective: Magnetic skyrmions—Overview of recent progress in an active research field
Everschor-Sitte, K., Masell, J., Reeve, R. M., & Kläui, M. (2018) · Journal of Applied Physics · doi:10.1063/1.5048972 · Cited by 601
Large-Area Soft e-Skin: The Challenges Beyond Sensor Designs
Dahiya, R., Yogeswaran, N., Liu, F., Manjakkal, L., Burdet, E., Hayward, V., & Jörntell, H. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2941366 · Cited by 309
Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing
Zahedinejad, M., Awad, A. A., Muralidhar, S., Khymyn, R., Fulara, H., Mazraati, H., Dvornik, M., & Åkerman, J. (2020) · Nature Nanotechnology · doi:10.1038/s41565-019-0593-9 · Cited by 284
Metadata
- Authors
- Grollier, J., Querlioz, D., & Stiles, M. D.
- Venue
- Proceedings of the IEEE
- Published
- 2016-10
- Volume
- 104
- Issue
- 10
- Pages
- 2024-2039
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2469329285
- Cited by
- 433
- References
- 23
- FWCI
- 23.97
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
