Memory-centric neuromorphic computing for unstructured data processing
Nano Research, ISSN: 1998-0000, Vol: 14, Issue: 9, Page: 3126-3142
2021
- 25Citations
- 5Usage
- 38Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations25
- Citation Indexes25
- 25
- CrossRef23
- Usage5
- Abstract Views5
- Captures38
- Readers38
- 38
Review Description
The unstructured data such as visual information, natural language, and human behaviors opens up a wide array of opportunities in the field of artificial intelligence (AI). The memory-centric neuromorphic computing (MNC) has been proposed for the efficient processing of unstructured data, bypassing the von Neumann bottleneck of current computing architecture. The development of MNC would provide massively parallel processing of unstructured data, realizing the cognitive AI in edge and wearable systems. In this review, recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories, volatile switches, synaptic plasticity, neuronal models, and memristive neural network. [Figure not available: see fulltext.]
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85104555377&origin=inward; http://dx.doi.org/10.1007/s12274-021-3452-6; https://link.springer.com/10.1007/s12274-021-3452-6; https://dc.tsinghuajournals.com/nano-research/vol14/iss9/24; https://dc.tsinghuajournals.com/cgi/viewcontent.cgi?article=4505&context=nano-research; http://sciencechina.cn/gw.jsp?action=cited_outline.jsp&type=1&id=7159088&internal_id=7159088&from=elsevier; https://dx.doi.org/10.1007/s12274-021-3452-6; https://link.springer.com/article/10.1007/s12274-021-3452-6
Tsinghua University Press
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