The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Survey of Stochastic Computing
Abstract
Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative to conventional binary computing. It is unique in that it represents and processes information in the form of digitized probabilities. SC employs very low-complexity arithmetic units which was a primary design concern in the past. Despite this advantage and also its inherent error tolerance, SC was seen as impractical because of very long computation times and relatively low accuracy. However, current technology trends tend to increase uncertainty in circuit behavior and imply a need to better understand, and perhaps exploit, probability in computation. This article surveys SC from a modern perspective where the small size, error resilience, and probabilistic features of SC may compete successfully with conventional methodologies in certain applications. First, we survey the literature and review the key concepts of stochastic number representation and circuit structure. We then describe the design of SC-based circuits and evaluate their advantages and disadvantages. Finally, we give examples of the potential applications of SC and discuss some practical problems that are yet to be solved.
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Citations
Cites
Low-density parity-check codes
Gallager, R. (1962) · IEEE Transactions on Information Theory · doi:10.1109/tit.1962.1057683 · Cited by 10,602
Factor graphs and the sum-product algorithm
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InfluentialNear Shannon limit performance of low density paritycheck codes
MacKay, D. J. C., & Neal, R. M. (1997) · Electronics Letters · doi:10.1049/el:19970362 · Cited by 2,678
Near Shannon limit performance of low density paritycheck codes
MacKay, D. J. C., & Neal, R. M. (1996) · Electronics Letters · doi:10.1049/el:19961141 · Cited by 1,923
Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components
von Neumann, J. (1956) · Cited by 1,652
Probabilistic Logics and the Synthesis of Reliable Organisms From Unreliable Components
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Cattermole, K. W. (1968) · Electronics and Power · doi:10.1049/ep.1968.0141 · Cited by 1,319
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Stochastic neural computation. I. Computational elements
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Mandyam N, A. (2021) · Mapana Journal of Sciences · doi:10.12723/mjs.56.5 · Cited by 357
Cited by
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289
Gokmen, T., & Vlasov, Y. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00333 · Cited by 491
Stochastic memristive devices for computing and neuromorphic applications
Gaba, S., Sheridan, P., Zhou, J., Choi, S., & Lu, W. (2013) · Nanoscale · doi:10.1039/c3nr01176c · Cited by 307
“proaches this is achieved via a stochastic number generator (SNG) 24). SNGs account for the majority of the resource usage in stochastic ( 9 computing, with a cost as high as 80% of the entire system (24). The ability to generate random bitstreams using memristive devices can represent an intriguing option for efficie…”
BackgroundA memristor-based Bayesian machine
Harabi, K.-E., Hirtzlin, T., Turck, C., Vianello, E., Laurent, R., Droulez, J., Bessière, P., Portal, J.-M., Bocquet, M., & Querlioz, D. (2022) · Nature Electronics · doi:10.1038/s41928-022-00886-9 · Cited by 58
Inherent stochasticity in phase-change memory devices
Le Gallo, M., Tuma, T., Zipoli, F., Sebastian, A., & Eleftheriou, E. (2016) · 2016 46th European Solid-State Device Research Conference (ESSDERC) · doi:10.1109/essderc.2016.7599664 · Cited by 35
Metadata
- Authors
- Alaghi, A., & Hayes, J. P.
- Venue
- ACM Transactions on Embedded Computing Systems
- Published
- 2013-05
- Volume
- 12
- Issue
- 2s
- Pages
- 1-19
- ISSN
- 1539-9087 (ISSN-L)
- 1558-3465
- OpenAlex
- W2003056114
- Cited by
- 591
- References
- 26
- FWCI
- 20.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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