Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing
Abstract
Ionic floating-gate memories: Digital implementations of artificial neural networks perform many tasks, such as image recognition and language processing, but are too energy intensive for many applications. Analog circuits that use large crossbar arrays of synaptic memory elements represent a low-power alternative, but most devices cannot update the synaptic weights uniformly or scale to large array sizes. Fuller et al. developed an integrated device, ionic floating-gate memory, that has the gate terminal of a redox transistor electrically connected to a diffusive memristor. This low-power device enabled linear and symmetric weight updates in parallel over an entire crossbar array at megahertz rates over 10 9 write-read cycles. Science , this issue p. 570
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In many materials, such as PCM, ReRAM, CBRAM, or ECRAM, conductances are effectively continuous physical quantities, and stored weights are not quantized.
“In many materials, such as phase-change memory (PCM)22,23, resistive random-access memory (ReRAM)24,25, conductive bridge RAM (CBRAM)26,27, or electro-chemical random-access memory (ECRAM)28,29, these conductances are effectively continuous physical quantities, and stored weights are not quantized.”
From Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators (2023) · cites this paper as [27]
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“…AIMC weight precision is impacted by various nonidealities, including thermal and 1/f noise, randomization during physical switching induced by electrical and thermal fl uctuations, material inhomogenities 30 , and device-to-device variability introduced during device fabrication or operation.”
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Self-citation
- Authors
- Fuller, E. J., Keene, S. T., Melianas, A., Wang, Z., Agarwal, S., Li, Y., Tuchman, Y., James, C. D., Marinella, M. J., Yang, J. J., Salleo, A., & Talin, A. A.
- Affiliations
- Sandia National Laboratories California (US)
- Stanford University (US)
- University of Massachusetts Amherst (US)
- Sandia National Laboratories (US)
- Corresponding author
- Keene, Scott T.
- Melianas, Armantas
- Salleo, Alberto
- Talin, A. Alec
- ORCID
- Venue
- Science
- Published
- 2019-05-10
- Volume
- 364
- Issue
- 6440
- Pages
- 570-574
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2942216650
- Cited by
- 756
- References
- 7
- Citations by year
- 2026: 65 · 2025: 112 · 2024: 110 · 2023: 108 · 2022: 125 · 2021: 120 · 2020: 93 · 2019: 23
- FWCI
- 32.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (1739795)
- Basic Energy Sciences (DESC0001160)
- Sandia National Laboratories’ Laboratory Directed Research and Development Program
- 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 › Materials Science › Polymers and Plastics › Conducting polymers and applications
- Keywords
- neuromorphic computing, crossbar memory arrays, conductive bridge memory, synaptic plasticity, non-volatile memory
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