The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Finding a roadmap to achieve large neuromorphic hardware systems
Open accessAbstract
Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Toward this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore's law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.
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Cited by
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Recent advances in physical reservoir computing: A review
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Neuro-Inspired Computing With Emerging Nonvolatile Memorys
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Neuromorphic photonic networks using silicon photonic weight banks
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Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine
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Metadata
- Authors
- Hasler, J., & Marr, B.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 7
- Pages
- 118-118
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2040429262
- Cited by
- 392
- References
- 16
- FWCI
- 17.17
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
