The DOI matches its Crossref record.
A Survey of Techniques for Approximate Computing
Open accessAbstract
Approximate computing trades off computation quality with effort expended, and as rising performance demands confront plateauing resource budgets, approximate computing has become not merely attractive, but even imperative. In this article, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU, and FPGA), processor components, memory technologies, and so forth, as well as programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this article is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Brian 2, an intuitive and efficient neural simulator
Stimberg, M., Brette, R., & Goodman, D. F. (2019) · eLife · doi:10.7554/elife.47314 · Cited by 749
A survey of FPGA-based accelerators for convolutional neural networks
Mittal, S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-018-3761-1 · Cited by 429
Self-citationA survey of data partitioning and sampling methods to support big data analysis
Mahmud, M. S., Huang, J. Z., Salloum, S., Emara, T. Z., & Sadatdiynov, K. (2020) · Big Data Mining and Analytics · doi:10.26599/bdma.2019.9020015 · Cited by 282
A survey of techniques for optimizing deep learning on gpus
Mittal, S., & Vaishay, S. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101635 · Cited by 197
Self-citationA survey on modeling and improving reliability of DNN algorithms and accelerators
Mittal, S. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101689 · Cited by 175
Self-citation
Metadata
- Authors
- Mittal, S.
- Venue
- ACM Computing Surveys
- Published
- 2016-03-18
- Volume
- 48
- Issue
- 4
- Pages
- 1-33
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2265166184
- Cited by
- 1,058
- References
- 0
- FWCI
- 52.60
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Survey of Techniques for Approximate Computing. https://app.cochat.ai/papers/10.1145/2893356
- The paper
- Mittal, S. (2016). A Survey of Techniques for Approximate Computing. ACM Computing Surveys. https://doi.org/10.1145/2893356
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
