ACM Computing Surveys· 2016Q1· Review
A Survey of Techniques for Approximate Computing
- 1,042citations
- Q1SCImago
- 2016year
Short summary
This survey categorizes and analyzes various techniques for approximate computing, which sacrifices computation quality for efficiency, highlighting its growing necessity due to increasing performance demands and limited resources.
AI-generated from the title and abstract; the full text is not read.
Key points
- Approximate computing offers a trade-off between computation quality and resource expenditure.
- Techniques cover program portion identification, quality monitoring, and application across diverse hardware (CPU, GPU, FPGA).
- The survey classifies AC methods based on key characteristics to highlight similarities and differences.
- The field is presented as increasingly imperative due to performance demands and resource constraints.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. ACM Computing Surveys, 2016 · DOI ↗
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Field: Hardware and Architecture
Hardware and ArchitectureComputer Science