Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
Open accessAbstract
Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.
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Memristive crossbars can be fabricated with very small areal footprint.
“Memristive crossbars can also be fabricated with very small areal footprint27,29,60.”
From Neuromorphic computing with multi-memristive synapses (2018) · cites this paper as [60]
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Self-citation
- Authors
- Eryilmaz, S. B., Kuzum, D., Jeyasingh, R., Kim, S., BrightSky, M., Lam, C., & Wong, H.-S. P.
- Affiliations
- Stanford University (US)
- University of Pennsylvania (US)
- IBM Research - Thomas J. Watson Research Center (US)
- Corresponding author
- Eryilmaz, Sukru B.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2014-07-22
- Volume
- 8
- Pages
- 205-205
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC4106403
- OpenAlex
- W2032959979
- Cited by
- 205
- References
- 9
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 8 · 2023: 18 · 2022: 18 · 2021: 23 · 2020: 33 · 2019: 23 · 2018: 26 · 2017: 17 · 2016: 11 · 2015: 15 · 2014: 2
- FWCI
- 7.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1317470)
- Semiconductor Research Corporation
- Microelectronics Advanced Research Corporation
- Defense Advanced Research Projects Agency
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Materials Science › Materials Chemistry › Phase-change materials and chalcogenides
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- associative memory, phase-change memory, artificial synapses, neuromorphic computing, Hebbian learning, energy consumption, nanoscale memory
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