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Accelerating machine learning with Non-Volatile Memory: Exploring device and circuit tradeoffs
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Large arrays of the same nonvolatile memories (NVM) being developed for Storage-Class Memory (SCM) - such as Phase Change Memory (PCM) and Resistance RAM (ReRAM) - can also be used in non-Von Neumann neuromorphic computational schemes, with device conductance serving as synaptic “weight.” This allows the all-important multiply-accumulate operation within these algorithms to be performed efficiently at the weight data.
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As long as the drift exponent is small enough (<0.1; in our devices it is on average 0.05), it is not very detrimental for neural network applications.
“As described recently62, as long as the drift exponent is small enough (<0.1; in our devices it is on average 0.05, see Supplementary Note 4), it is not very detrimental for neural network applications.”
From Neuromorphic computing with multi-memristive synapses (2018) · cites this paper as [62]
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Metadata
- Authors
- Fumarola, A., Narayanan, P., Sanches, L. L., Sidler, S., Jang, J., Moon, K., Shelby, R. M., Hwang, H., & Burr, G. W.
- Affiliations
- IBM Research - Almaden (US)
- Pohang University of Science and Technology (KR)
- ORCID
- Venue
- 2016 IEEE International Conference on Rebooting Computing (ICRC)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- 1-8
- OpenAlex
- W2550153070
- Cited by
- 37
- References
- 9
- Citations by year
- 2023: 4 · 2022: 3 · 2021: 4 · 2020: 6 · 2019: 6 · 2018: 7 · 2017: 7
- FWCI
- 9.38
- Type
- conference paper
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE International Conference on Rebooting Computing (ICRC) (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- 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 › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- non-volatile memory, storage class memory, phase-change memory, resistive random access memory, neuromorphic computing, in-memory computing, multiply-accumulate operations, synaptic weights, machine learning acceleration
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