Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate Computing

Approximate computing has emerged as an efficient solution for energy saving at the expense of calculation accuracy, especially for floating-point operation intensive applications, which have urgent demands for some uniform design frameworks for floating-point approximate computing combining the app...

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Main Authors: Yiyao Xiang, Lei Li, Shiwei Yuan, Wanting Zhou, Benqing Guo
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9333619/
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spelling doaj-058ace2e38b84855ab9fabc98627198b2021-05-27T23:02:58ZengIEEEIEEE Access2169-35362021-01-019710397105210.1109/ACCESS.2021.30535789333619Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate ComputingYiyao Xiang0https://orcid.org/0000-0003-0589-9417Lei Li1Shiwei Yuan2https://orcid.org/0000-0002-6989-6605Wanting Zhou3Benqing Guo4https://orcid.org/0000-0001-5376-4942University of Electronic Science and Technology of China, Chengdu, ChinaUniversity of Electronic Science and Technology of China, Chengdu, ChinaUniversity of Electronic Science and Technology of China, Chengdu, ChinaUniversity of Electronic Science and Technology of China, Chengdu, ChinaCollege of Communication Engineering, Chengdu University of Information Technology, Chengdu, ChinaApproximate computing has emerged as an efficient solution for energy saving at the expense of calculation accuracy, especially for floating-point operation intensive applications, which have urgent demands for some uniform design frameworks for floating-point approximate computing combining the approximate computing techniques with the metrics of applications. In this paper, a simple approximate method with a zero-mean noise for the mantissa was introduced firstly, called PAM. Secondly, based on the proposed approximate method, the corresponding noise propagation models for floating-point operations were built, including floating-point addition, subtraction, and multiplication. Thirdly, a uniform design framework, which is only related to the operational-level topology of applications, was presented. The presented design framework can be used to evaluate the quality of data produced by applications before the circuit design is completed, and the efficient bit width of the mantissa can be obtained under specific requirements, which is also suitable for truncation. Finally, we studied the feasibility of the proposed design framework through two typical applications of image processing, edge detection and Gaussian filtering. The experimental results of edge detection have shown that our proposed design framework could effectively predict efficient bit width under the specific peak signal-to-noise ratio, with a difference of 1–2 bits in extreme situations. The Gaussian filtering experiment has demonstrated that the proposed design framework could apply to applications with complex calculations and structures.https://ieeexplore.ieee.org/document/9333619/Approximate computingefficient bit widthnoise propagation modelsfloating-pointmantissatruncation
collection DOAJ
language English
format Article
sources DOAJ
author Yiyao Xiang
Lei Li
Shiwei Yuan
Wanting Zhou
Benqing Guo
spellingShingle Yiyao Xiang
Lei Li
Shiwei Yuan
Wanting Zhou
Benqing Guo
Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate Computing
IEEE Access
Approximate computing
efficient bit width
noise propagation models
floating-point
mantissa
truncation
author_facet Yiyao Xiang
Lei Li
Shiwei Yuan
Wanting Zhou
Benqing Guo
author_sort Yiyao Xiang
title Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate Computing
title_short Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate Computing
title_full Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate Computing
title_fullStr Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate Computing
title_full_unstemmed Metrics, Noise Propagation Models, and Design Framework for Floating-Point Approximate Computing
title_sort metrics, noise propagation models, and design framework for floating-point approximate computing
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description Approximate computing has emerged as an efficient solution for energy saving at the expense of calculation accuracy, especially for floating-point operation intensive applications, which have urgent demands for some uniform design frameworks for floating-point approximate computing combining the approximate computing techniques with the metrics of applications. In this paper, a simple approximate method with a zero-mean noise for the mantissa was introduced firstly, called PAM. Secondly, based on the proposed approximate method, the corresponding noise propagation models for floating-point operations were built, including floating-point addition, subtraction, and multiplication. Thirdly, a uniform design framework, which is only related to the operational-level topology of applications, was presented. The presented design framework can be used to evaluate the quality of data produced by applications before the circuit design is completed, and the efficient bit width of the mantissa can be obtained under specific requirements, which is also suitable for truncation. Finally, we studied the feasibility of the proposed design framework through two typical applications of image processing, edge detection and Gaussian filtering. The experimental results of edge detection have shown that our proposed design framework could effectively predict efficient bit width under the specific peak signal-to-noise ratio, with a difference of 1–2 bits in extreme situations. The Gaussian filtering experiment has demonstrated that the proposed design framework could apply to applications with complex calculations and structures.
topic Approximate computing
efficient bit width
noise propagation models
floating-point
mantissa
truncation
url https://ieeexplore.ieee.org/document/9333619/
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