Tools for Reduced Precision Computation: A Survey

The use of reduced precision to improve performance metrics such as computation latency and power consumption is a common practice in the embedded systems field. This practice is emerging as a new trend in High Performance Computing (HPC), especially when new error-tolerant applications are consider...

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Bibliographic Details
Published in:ACM computing surveys 2021-03, Vol.53 (2), p.1-35, Article 33
Main Authors: Cherubin, Stefano, Agosta, Giovanni
Format: Article
Language:English
Subjects:
Approximation
Automation
Compilers
Computation
Computer science
Computer systems organization
Document types
Embedded and cyber-physical systems
Embedded software
Embedded systems
Functional analysis
General and reference
Mathematical analysis
Mathematics of computing
Performance enhancement
Performance measurement
Power consumption
Surveys and overviews
Citations: Items that this one cites
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Summary:The use of reduced precision to improve performance metrics such as computation latency and power consumption is a common practice in the embedded systems field. This practice is emerging as a new trend in High Performance Computing (HPC), especially when new error-tolerant applications are considered. However, standard compiler frameworks do not support automated precision customization, and manual tuning and code transformation is the approach usually adopted in most domains. In recent years, research have been studying ways to improve the automation of this process. This article surveys this body of work, identifying the critical steps of this process, the most advanced tools available, and the open challenges in this research area. We conclude that, while several mature tools exist, there is still a gap to close, especially for tools based on static analysis rather than profiling, as well as for integration within mainstream, industry-strength compiler frameworks.
ISSN:0360-0300
1557-7341
DOI:10.1145/3381039