The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Large-scale neuromorphic computing systems
Open accessAbstract
Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the late 1980s. This early work influenced others to carry developments forward, and advances in VLSI technology supported steady growth in the scale and capability of neuromorphic devices. Recently, a number of large-scale neuromorphic projects have emerged, taking the approach to unprecedented scales and capabilities. These large-scale projects are associated with major new funding initiatives for brain-related research, creating a sense that the time and circumstances are right for progress in our understanding of information processing in the brain. In this review we present a brief history of neuromorphic engineering then focus on some of the principal current large-scale projects, their main features, how their approaches are complementary and distinct, their advantages and drawbacks, and highlight the sorts of capabilities that each can deliver to neural modellers.
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Cites
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BackgroundA million spiking-neuron integrated circuit with a scalable communication network and interface
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Analog VLSI and Neural Systems
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Self-citationNeurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations
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A wafer-scale neuromorphic hardware system for large-scale neural modeling
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Neuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics
Park, H., Lee, Y., Kim, N., Seo, D., Go, G., & Lee, T. (2020) · Advanced Materials · doi:10.1002/adma.201903558 · Cited by 533
Wang, M., Luo, Y., Wang, T., Wan, C., Pan, L., Pan, S., He, K., Neo, A., & Chen, X. (2021) · Advanced Materials · doi:10.1002/adma.202003014 · Cited by 451
Metadata
- Authors
- Furber, S.
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2016-08-16
- Volume
- 13
- Issue
- 5
- Pages
- 051001
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2513148968
- Cited by
- 567
- References
- 29
- FWCI
- 16.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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