Tampere University of Technology

TUTCRIS Research Portal

Instrumentation-Driven Validation of Dataflow Applications

Research output: Contribution to journalArticleScientificpeer-review

Standard

Instrumentation-Driven Validation of Dataflow Applications. / Chukhman, Ilya; Jiao, Yang; Salem, Haifa Ben; Bhattacharyya, Shuvra S.

In: Journal of Signal Processing Systems, Vol. 84, No. 3, 2016, p. 383–397.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Chukhman, I, Jiao, Y, Salem, HB & Bhattacharyya, SS 2016, 'Instrumentation-Driven Validation of Dataflow Applications', Journal of Signal Processing Systems, vol. 84, no. 3, pp. 383–397. https://doi.org/10.1007/s11265-015-1073-6

APA

Chukhman, I., Jiao, Y., Salem, H. B., & Bhattacharyya, S. S. (2016). Instrumentation-Driven Validation of Dataflow Applications. Journal of Signal Processing Systems, 84(3), 383–397. https://doi.org/10.1007/s11265-015-1073-6

Vancouver

Chukhman I, Jiao Y, Salem HB, Bhattacharyya SS. Instrumentation-Driven Validation of Dataflow Applications. Journal of Signal Processing Systems. 2016;84(3):383–397. https://doi.org/10.1007/s11265-015-1073-6

Author

Chukhman, Ilya ; Jiao, Yang ; Salem, Haifa Ben ; Bhattacharyya, Shuvra S. / Instrumentation-Driven Validation of Dataflow Applications. In: Journal of Signal Processing Systems. 2016 ; Vol. 84, No. 3. pp. 383–397.

Bibtex - Download

@article{723796a2056747c4aa491f7a26c8b41f,
title = "Instrumentation-Driven Validation of Dataflow Applications",
abstract = "Dataflow modeling offers a myriad of tools for designing and optimizing signal processing systems. A designer is able to take advantage of dataflow properties to effectively tune the system in connection with functionality and different performance metrics. However, a disparity in the specification of dataflow properties and the final implementation can lead to incorrect behavior that is difficult to detect. This motivates the problem of ensuring consistency between dataflow properties that are declared or otherwise assumed as part of dataflow-based application models, and the dataflow behavior that is exhibited by implementations that are derived from the models. In this paper, we address this problem by introducing a novel dataflow validation framework (DVF) that is able to identify disparities between an application’s formal dataflow representation and its implementation. DVF works by instrumenting the implementation of an application and monitoring the instrumentation data as the application executes. This monitoring process is streamlined so that DVF achieves validation without major overhead. We demonstrate the utility of our DVF through design and implementation case studies involving an automatic speech recognition application, a JPEG encoder, and an acoustic tracking application.",
keywords = "Dataflow graphs, Design validation, Models of computation, Signal processing systems",
author = "Ilya Chukhman and Yang Jiao and Salem, {Haifa Ben} and Bhattacharyya, {Shuvra S.}",
year = "2016",
doi = "10.1007/s11265-015-1073-6",
language = "English",
volume = "84",
pages = "383–397",
journal = "Journal of Signal Processing Systems",
issn = "1939-8018",
publisher = "Springer Verlag",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Instrumentation-Driven Validation of Dataflow Applications

AU - Chukhman, Ilya

AU - Jiao, Yang

AU - Salem, Haifa Ben

AU - Bhattacharyya, Shuvra S.

PY - 2016

Y1 - 2016

N2 - Dataflow modeling offers a myriad of tools for designing and optimizing signal processing systems. A designer is able to take advantage of dataflow properties to effectively tune the system in connection with functionality and different performance metrics. However, a disparity in the specification of dataflow properties and the final implementation can lead to incorrect behavior that is difficult to detect. This motivates the problem of ensuring consistency between dataflow properties that are declared or otherwise assumed as part of dataflow-based application models, and the dataflow behavior that is exhibited by implementations that are derived from the models. In this paper, we address this problem by introducing a novel dataflow validation framework (DVF) that is able to identify disparities between an application’s formal dataflow representation and its implementation. DVF works by instrumenting the implementation of an application and monitoring the instrumentation data as the application executes. This monitoring process is streamlined so that DVF achieves validation without major overhead. We demonstrate the utility of our DVF through design and implementation case studies involving an automatic speech recognition application, a JPEG encoder, and an acoustic tracking application.

AB - Dataflow modeling offers a myriad of tools for designing and optimizing signal processing systems. A designer is able to take advantage of dataflow properties to effectively tune the system in connection with functionality and different performance metrics. However, a disparity in the specification of dataflow properties and the final implementation can lead to incorrect behavior that is difficult to detect. This motivates the problem of ensuring consistency between dataflow properties that are declared or otherwise assumed as part of dataflow-based application models, and the dataflow behavior that is exhibited by implementations that are derived from the models. In this paper, we address this problem by introducing a novel dataflow validation framework (DVF) that is able to identify disparities between an application’s formal dataflow representation and its implementation. DVF works by instrumenting the implementation of an application and monitoring the instrumentation data as the application executes. This monitoring process is streamlined so that DVF achieves validation without major overhead. We demonstrate the utility of our DVF through design and implementation case studies involving an automatic speech recognition application, a JPEG encoder, and an acoustic tracking application.

KW - Dataflow graphs

KW - Design validation

KW - Models of computation

KW - Signal processing systems

U2 - 10.1007/s11265-015-1073-6

DO - 10.1007/s11265-015-1073-6

M3 - Article

VL - 84

SP - 383

EP - 397

JO - Journal of Signal Processing Systems

JF - Journal of Signal Processing Systems

SN - 1939-8018

IS - 3

ER -