Compressed sensing recovery using computational memory
Open accessAbstract
Computational memory (CM) is a promising non-von Neumann approach where certain computational tasks are performed within resistive memory units by exploiting their physical attributes. We propose a new method for fast and robust compressed sensing (CS) recovery of sparse signals using CM. For a signal of size N, this method achieves a potential O(N)-fold complexity reduction compared with a standard software approach. Large-scale experimental demonstrations using more than 256k phase-change memory (PCM) devices are presented along with an in-depth device analysis and array-level considerations.
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Memristive devices can be read with low energy (10-100 fJ for our devices), leading to substantially lower energy consumption than in conventional von Neumann systems.
“Note that memristive devices can be read with low energy (10–100 fJ for our devices), which leads to a substantially lower energy consumption than in conventional von Neumann systems57–59.”
From Neuromorphic computing with multi-memristive synapses (2018) · cites this paper as [57]
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References 6 · Cited by 28
- Authors
- Le Gallo, M., Sebastian, A., Cherubini, G., Giefers, H., & Eleftheriou, E.
- Affiliations
- IBM Research - Zurich (CH)
- Venue
- 2017 IEEE International Electron Devices Meeting (IEDM)
- Publisher
- IEEE
- Published
- 2017-12
- Pages
- 28.3.1-28.3.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W2787624560
- Cited by
- 28
- References
- 6
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 1 · 2021: 4 · 2020: 4 · 2019: 5 · 2018: 6 · 2017: 1
- FWCI
- 6.69
- Type
- conference paper
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- cc-by
- Locations
- Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- compressed sensing, sparse signal recovery, phase-change memory, resistive random access memory, non-von Neumann architecture, hardware acceleration, computational complexity reduction
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