Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction
Open accessAbstract
We demonstrate a unique energy efficient methodology to use Phase Change Memory (PCM) as synapse in ultra-dense large scale neuromorphic systems. PCM devices with different chalcogenide materials were characterized to demonstrate synaptic behavior. Multi-physical simulations were used to interpret the results. We propose special circuit architecture (“the 2-PCM synapse”), read, write, and reset programming schemes suitable for the use of PCM in neural networks. A versatile behavioral model of PCM which can be used for simulating large scale neural systems is introduced. First demonstration of complex visual pattern extraction from real world data using PCM synapses in a 2-layer spiking neural network (SNN) is shown. System power analysis for different scaled PCM technologies is also provided.
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A non-differential architecture would have a lower implementation complexity than its differential counterpart because the refresh operation can be completely avoided.
“A non-differential architecture would have a lower implementation complexity than its differential counterpart because the refresh operation34,37, which requires reading and reprogramming G+ and G−, can be completely avoided.”
From Neuromorphic computing with multi-memristive synapses (2018) · cites this paper as [37]
Cites
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“A non-differential architecture would have a reduced implementation complexity compared to its differential counterpart because the refresh operation 33,36 , which requires reading and reprogramming G+ and G, can be completely avoided.”
BackgroundInfluentialA review of emerging non-volatile memory (NVM) technologies and applications
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- Authors
- Suri, M., Bichler, O., Querlioz, D., Cueto, O., Perniola, L., Sousa, V., Vuillaume, D., Gamrat, C., & DeSalvo, B.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Centre National de la Recherche Scientifique (FR)
- Venue
- 2011 International Electron Devices Meeting
- Publisher
- IEEE
- Published
- 2011-12
- Pages
- 4.4.1-4.4.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W1991059746
- Cited by
- 314
- References
- 2
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 18 · 2023: 24 · 2022: 23 · 2021: 29 · 2020: 42 · 2019: 37 · 2018: 33 · 2017: 27 · 2016: 16 · 2015: 21 · 2014: 4 · 2013: 12 · 2012: 10
- FWCI
- 11.28
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- cc-by-nc
- Locations
- Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- 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
- phase-change memory, neuromorphic hardware, chalcogenide materials, synaptic behavior, spiking neural networks, energy efficiency, power analysis, multiphysics simulation
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