An Integrated Hardware/Software Design Methodology for Signal Processing Systems

Lin Li, Carlo Sau, Tiziana Fanni, Jingui Li, Timo Viitanen, Francois Christophe, Francesca Palumbo, Luigi Raffo, Heikki Huttunen, Jarmo Takala, Shuvra S. Bhattacharyya

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper presents a new methodology for design and implementation of signal processing systems on system-on-chip (SoC) platforms. The methodology is centered on the use of lightweight application programming interfaces for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. As a demonstration of the proposed design framework, we present a dataflow-based deep neural network (DNN) implementation for vehicle classification that is streamlined for real-time operation on embedded SoC devices. Using the proposed methodology, we apply and integrate a variety of dataflow graph optimizations that are important for efficient mapping of the DNN system into a resource constrained implementation that involves cooperating multicore CPUs and field-programmable gate array subsystems. Through experiments, we demonstrate the flexibility and effectiveness with which different design transformations can be applied and integrated across multiple scales of the targeted computing system.
Original languageEnglish
JournalJournal of Systems Architecture
Volume93
Pages (from-to)1-19
Number of pages19
ISSN1383-7621
DOIs
Publication statusPublished - Feb 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 113 Computer and information sciences

Cite this

Li, L., Sau, C., Fanni, T., Li, J., Viitanen, T., Christophe, F., ... Bhattacharyya, S. S. (2019). An Integrated Hardware/Software Design Methodology for Signal Processing Systems. Journal of Systems Architecture, 93, 1-19. https://doi.org/10.1016/j.sysarc.2018.12.010
Li, Lin ; Sau, Carlo ; Fanni, Tiziana ; Li, Jingui ; Viitanen, Timo ; Christophe, Francois ; Palumbo, Francesca ; Raffo, Luigi ; Huttunen, Heikki ; Takala, Jarmo ; Bhattacharyya, Shuvra S. / An Integrated Hardware/Software Design Methodology for Signal Processing Systems. In: Journal of Systems Architecture. 2019 ; Vol. 93. pp. 1-19.
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title = "An Integrated Hardware/Software Design Methodology for Signal Processing Systems",
abstract = "This paper presents a new methodology for design and implementation of signal processing systems on system-on-chip (SoC) platforms. The methodology is centered on the use of lightweight application programming interfaces for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. As a demonstration of the proposed design framework, we present a dataflow-based deep neural network (DNN) implementation for vehicle classification that is streamlined for real-time operation on embedded SoC devices. Using the proposed methodology, we apply and integrate a variety of dataflow graph optimizations that are important for efficient mapping of the DNN system into a resource constrained implementation that involves cooperating multicore CPUs and field-programmable gate array subsystems. Through experiments, we demonstrate the flexibility and effectiveness with which different design transformations can be applied and integrated across multiple scales of the targeted computing system.",
keywords = "113 Computer and information sciences",
author = "Lin Li and Carlo Sau and Tiziana Fanni and Jingui Li and Timo Viitanen and Francois Christophe and Francesca Palumbo and Luigi Raffo and Heikki Huttunen and Jarmo Takala and Bhattacharyya, {Shuvra S.}",
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Li, L, Sau, C, Fanni, T, Li, J, Viitanen, T, Christophe, F, Palumbo, F, Raffo, L, Huttunen, H, Takala, J & Bhattacharyya, SS 2019, 'An Integrated Hardware/Software Design Methodology for Signal Processing Systems', Journal of Systems Architecture, vol. 93, pp. 1-19. https://doi.org/10.1016/j.sysarc.2018.12.010

An Integrated Hardware/Software Design Methodology for Signal Processing Systems. / Li, Lin; Sau, Carlo; Fanni, Tiziana; Li, Jingui; Viitanen, Timo ; Christophe, Francois; Palumbo, Francesca; Raffo, Luigi; Huttunen, Heikki; Takala, Jarmo; Bhattacharyya, Shuvra S.

In: Journal of Systems Architecture, Vol. 93, 02.2019, p. 1-19.

Research output: Contribution to journalArticleScientificpeer-review

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T1 - An Integrated Hardware/Software Design Methodology for Signal Processing Systems

AU - Li, Lin

AU - Sau, Carlo

AU - Fanni, Tiziana

AU - Li, Jingui

AU - Viitanen, Timo

AU - Christophe, Francois

AU - Palumbo, Francesca

AU - Raffo, Luigi

AU - Huttunen, Heikki

AU - Takala, Jarmo

AU - Bhattacharyya, Shuvra S.

PY - 2019/2

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N2 - This paper presents a new methodology for design and implementation of signal processing systems on system-on-chip (SoC) platforms. The methodology is centered on the use of lightweight application programming interfaces for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. As a demonstration of the proposed design framework, we present a dataflow-based deep neural network (DNN) implementation for vehicle classification that is streamlined for real-time operation on embedded SoC devices. Using the proposed methodology, we apply and integrate a variety of dataflow graph optimizations that are important for efficient mapping of the DNN system into a resource constrained implementation that involves cooperating multicore CPUs and field-programmable gate array subsystems. Through experiments, we demonstrate the flexibility and effectiveness with which different design transformations can be applied and integrated across multiple scales of the targeted computing system.

AB - This paper presents a new methodology for design and implementation of signal processing systems on system-on-chip (SoC) platforms. The methodology is centered on the use of lightweight application programming interfaces for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. As a demonstration of the proposed design framework, we present a dataflow-based deep neural network (DNN) implementation for vehicle classification that is streamlined for real-time operation on embedded SoC devices. Using the proposed methodology, we apply and integrate a variety of dataflow graph optimizations that are important for efficient mapping of the DNN system into a resource constrained implementation that involves cooperating multicore CPUs and field-programmable gate array subsystems. Through experiments, we demonstrate the flexibility and effectiveness with which different design transformations can be applied and integrated across multiple scales of the targeted computing system.

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