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Simplified performance evaluation of floating-point formats for implementing intelligent measurement systems.

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Title: Simplified performance evaluation of floating-point formats for implementing intelligent measurement systems.
Authors: Dinčić, Milan R1 (AUTHOR) milan.dincic@elfak.ni.ac.rs, Perić, Zoran H1 (AUTHOR), Denić, Dragan B1 (AUTHOR), Denić, Bojan D1 (AUTHOR), Perić, Sofija Z1 (AUTHOR)
Source: Transactions of the Institute of Measurement & Control. Jun2026, Vol. 48 Issue 9, p1992-2002. 11p.
Subjects: Floating-point arithmetic, Wireless sensor nodes, Benchmark problems (Computer science), Sensor networks, Measuring instruments, Signal-to-noise ratio, Gaussian distribution, Artificial neural networks
Abstract: This paper provides a simple method for efficient performance evaluation of floating-point (FP) formats, addressing the challenge of implementing DNN (deep neural network)-based sensor nodes and edge measurement devices. Since resource constraints are imposed in such scenarios, the 32-bit FP format, standardly used for DNN implementation, is unsuitable, and the alternative is found in lower-resolution FP formats which leads to certain performance degradation. Hence, an efficient mechanism for performance evaluation of different FP formats is needed to examine the influence of resolution decreasing. Existing methods utilize the analogy between FP formats and piecewise uniform quantizers (PUQs), using the signal-to-quantization noise ratio (SQNR) of the PUQ to express the performance of FP formats. However, the high complexity of SQNR calculations, involving sum with many terms (e.g. 254 terms for FP formats with an 8-bit exponent), poses a significant challenge. This paper's main contribution is the significant simplification of the SQNR expression for Gaussian-distributed data, reducing the number of sum terms from 254 to just 5 with minimal accuracy loss, allowing for simple and efficient performance evaluation of FP formats. Major findings include an in-depth analysis of the probability distribution across PUQ segments, a closed-form expression for identifying the highest probability segment, and an evaluation of the SQNR approximation's accuracy. These findings provide a foundational basis for implementing intelligent DNN-based measurement systems, with applications extending to computing, signal processing, and other fields utilizing FP formats. [ABSTRACT FROM AUTHOR]
Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 193925921
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Simplified performance evaluation of floating-point formats for implementing intelligent measurement systems.
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  Data: <searchLink fieldCode="AR" term="%22Dinčić%2C+Milan+R%22">Dinčić, Milan R</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> milan.dincic@elfak.ni.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Perić%2C+Zoran+H%22">Perić, Zoran H</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Denić%2C+Dragan+B%22">Denić, Dragan B</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Denić%2C+Bojan+D%22">Denić, Bojan D</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Perić%2C+Sofija+Z%22">Perić, Sofija Z</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Transactions+of+the+Institute+of+Measurement+%26+Control%22">Transactions of the Institute of Measurement & Control</searchLink>. Jun2026, Vol. 48 Issue 9, p1992-2002. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Floating-point+arithmetic%22">Floating-point arithmetic</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+nodes%22">Wireless sensor nodes</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Measuring+instruments%22">Measuring instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+distribution%22">Gaussian distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper provides a simple method for efficient performance evaluation of floating-point (FP) formats, addressing the challenge of implementing DNN (deep neural network)-based sensor nodes and edge measurement devices. Since resource constraints are imposed in such scenarios, the 32-bit FP format, standardly used for DNN implementation, is unsuitable, and the alternative is found in lower-resolution FP formats which leads to certain performance degradation. Hence, an efficient mechanism for performance evaluation of different FP formats is needed to examine the influence of resolution decreasing. Existing methods utilize the analogy between FP formats and piecewise uniform quantizers (PUQs), using the signal-to-quantization noise ratio (SQNR) of the PUQ to express the performance of FP formats. However, the high complexity of SQNR calculations, involving sum with many terms (e.g. 254 terms for FP formats with an 8-bit exponent), poses a significant challenge. This paper's main contribution is the significant simplification of the SQNR expression for Gaussian-distributed data, reducing the number of sum terms from 254 to just 5 with minimal accuracy loss, allowing for simple and efficient performance evaluation of FP formats. Major findings include an in-depth analysis of the probability distribution across PUQ segments, a closed-form expression for identifying the highest probability segment, and an evaluation of the SQNR approximation's accuracy. These findings provide a foundational basis for implementing intelligent DNN-based measurement systems, with applications extending to computing, signal processing, and other fields utilizing FP formats. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
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        Value: 10.1177/01423312251319575
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1992
    Subjects:
      – SubjectFull: Floating-point arithmetic
        Type: general
      – SubjectFull: Wireless sensor nodes
        Type: general
      – SubjectFull: Benchmark problems (Computer science)
        Type: general
      – SubjectFull: Sensor networks
        Type: general
      – SubjectFull: Measuring instruments
        Type: general
      – SubjectFull: Signal-to-noise ratio
        Type: general
      – SubjectFull: Gaussian distribution
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
    Titles:
      – TitleFull: Simplified performance evaluation of floating-point formats for implementing intelligent measurement systems.
        Type: main
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            NameFull: Dinčić, Milan R
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            NameFull: Perić, Zoran H
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            NameFull: Denić, Dragan B
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            NameFull: Denić, Bojan D
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 48
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              Value: 9
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