A memristive deep belief neural network based on silicon synapses
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Various studies have demonstrated that the energy efficiency, hardware size, and inference speed of memristor crossbar arrays can be effectively improved compared with conventional CMOS-based processors.
“Based on the desirable characteristics of the memristor crossbar array, various studies have demonstrated that the energy efficiency, hardware size, and inference speed can be effectively improved compared with the conventional CMOS-based processors 12,17,18”
From Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array (2023) · cites this paper as [17]
The Memristive synaptic array section (p. 5) reports estimated improvements in energy efficiency and performance density for array operations compared with a Tesla V100 GPU. The Methods (p. 15) say these estimates exclude peripheral-circuit energy and footprint. Performance density is not a direct comparison of total hardware size, and the paper does not establish improved inference speed over a conventional CMOS processor. (LLM, conf=0.88)
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Cites
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Cited by
Sorted by citations
Park, S.-O., Park, T., Jeong, H., Hong, S., Seo, S., Kwon, Y., Lee, J., & Choi, S. (2023) · Nanoscale Horizons · doi:10.1039/d3nh00121k · Cited by 31 · see citing works
- Authors
- Wang, W., Danial, L., Li, Y., Herbelin, E., Pikhay, E., Roizin, Y., Hoffer, B., Wang, Z., & Kvatinsky, S.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Tower Semiconductor (Israel) (IL)
- University of Hong Kong (HK)
- Corresponding author
- Wang, Wei
- Kvatinsky, Shahar
- ORCID
- Venue
- Nature Electronics
- Published
- 2022-12-19
- Volume
- 5
- Issue
- 12
- Pages
- 870-880
- ISSN
- 2520-1131 (ISSN-L)
- OpenAlex
- W4313430026
- Cited by
- 66
- References
- 13
- Citations by year
- 2026: 15 · 2025: 16 · 2024: 19 · 2023: 16
- FWCI
- 3.49
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Nature Electronics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (964877, 757259)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- memristors, floating-gate devices, neuromorphic computing, in situ training, CMOS fabrication, device-to-device variation, contrastive divergence, handwritten digit recognition
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