Addressing System-Level Optimization with OpenVX Graphs

Rainey, Erik; Villarreal, Jesse; Dedeoǧlu, Göksel; Pulli, Kari; Lepley, Thierry; Brill, Frank

doi:10.1109/cvprw.2014.100

Cited by 38 publications

(19 citation statements)

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“…Solutions such as Intel's Integrated Performance Primitives [Intel 2016b], OpenCV [Bradski andKaehler 2008] and OpenVX [Rainey et al 2014] abstract away from target-specific optimization problem by providing pre-implemented functions to the user. However, it may not be acceptable for a user to be dependent on a slow-changing vendor library.…”

Section: Heterogeneous Platform Programmabilitymentioning

confidence: 99%

Extending Halide to Improve Software Development for Imaging DSPs

Vocke

Corporaal

Jordans

et al. 2017

ACM Trans. Archit. Code Optim.

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MASTER Extending halide to improve software development for imaging DSPsVocke, S. Award date: 2016Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Specialized Digital Signal Processors (DSPs), which can be found in a wide range of modern devices, play an important role in power-efficient, high-performance image processing. Applications including camera sensor post-processing and computer vision can benefit from being (partially) mapped onto such DSPs. However, due to their specialized instruction sets and dependence on low-level code optimization, developing applications for DSPs is often more time-consuming and error-prone than for general-purpose processors. Halide is a domain-specific language (DSL) which enables low-effort development of portable, highperformance imaging pipelines -a combination of qualities which is currently hard, if not impossible to find among DSP programming models. I propose a set of extensions and modifications to Halide in order to support DSPs in combination with arbitrary C compilers, including a template solution to support diverse target instruction sets and heterogeneous scratchpad memories. Using a commercial Intel DSP, I demonstrate that this solution can be used to achieve performance comparable to tuned C code, while leading to a reduction in development time and code complexity. The results also show that DSPs are attractive alternatives to CPUs and GPUs for power-and area-efficient image processing using Halide. Categories and Subject Descriptors: [] ACM Reference Format:

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Section: Heterogeneous Platform Programmabilitymentioning

confidence: 99%

Extending Halide to Improve Software Development for Imaging DSPs

Vocke

Corporaal

Jordans

et al. 2017

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

show abstract

“…While system-level optimization issues, such as power consumption and memory bandwidth, are generally dealt within the engine layer, kernel optimization is locally achieved within the framework layer, where specific algorithms are refactored with more efficient implementation. Both approaches have their limitations: system-level optimization has to be addressed at the framework level, and kernel optimization has only a local effect on the efficiency of the entire algorithmic process (Rainey et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…OpenVX is a standardized interface, designed by the Khronos Group and released in late 2014, in an attempt to provide both system and kernel level optimization by modeling the system with graphs, which can be used for optimization and acceleration by the platform implementer (Rainey et al, 2014;Lin et al, 2015;Tagliavini et al, 2014). This model of standardization defers the responsibility for optimization from the user to the platform developer -separating the application and the hardware knowledge domains.…”

Section: Introductionmentioning

confidence: 99%

“…CUDA is a software layer, also developed by NVIDIA, that encapsulates GPU with a virtual instruction set, which enables a distributed execution of kernels. The development model of OpenVX is similar to the standardization of computer graphics provided by OpenGL (Rainey et al, 2014), which specifies an abstract interface for defining graphical components. OpenGL applications are extensively used in a wide spectrum of fields ranging from virtual reality to computeraided design.…”

Section: Introductionmentioning

confidence: 99%

“…Examples for OpenVX graph model for Canny edge detection (Canny, 1986), Sobel edge detection (Rainey et al, 2014), and histogram equalization (Lepley, 2015) are given in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

OpenVX-Based Python Framework for Real-time Cross-Platform Acceleration of Embedded Computer Vision Applications

Heimlich

Tsur

2016

Front. ICT

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Embedded real-time vision applications are being rapidly deployed in a large realm of consumer electronics, ranging from automotive safety to surveillance systems. However, the relatively limited computational power of embedded platforms is considered as a bottleneck for many vision applications, necessitating optimization. OpenVX is a standardized interface, released in late 2014, in an attempt to provide both system and kernel level optimization to vision applications. With OpenVX, Vision processing is modeled with coarse-grained data flow graphs, which can be optimized and accelerated by the platform implementer. Current full implementations of OpenVX are given in the programming language C, which neither supports advanced programming paradigms, such as object-oriented, imperative, and functional programming, nor does it has runtime or type checking. Here, we present a python-based full Implementation of OpenVX, which eliminates much of the discrepancies between the object-oriented paradigm used by many modern applications and the native C implementations. Our open-source implementation can be used for rapid development of OpenVX applications in embedded platforms. Demonstration includes static and real-time image acquisition and processing using a Raspberry Pi and a GoPro camera. Code is given in Supplementary Material. Code project and linked deployable virtual machine are located on GitHub: https://github.com/ NBEL-lab/PythonOpenVX.

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Integrating Simulink, OpenVX, and ROS for Model-Based Design of Embedded Vision Applications

Aldegheri

Bombieri

2019

VLSI-SoC: Opportunities and Challenges Beyond the Internet of Things

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OpenVX is increasingly gaining consensus as standard platform to develop portable, optimized and power-efficient embedded vision applications. Nevertheless, adopting OpenVX for rapid prototyping, early algorithm parametrization and validation of complex embedded applications is a very challenging task. This paper presents a comprehensive framework that integrates Simulink, OpenVX, and ROS for model-based design of embedded vision applications. The framework allows applying Matlab-Simulink for the model-based design, parametrization, and validation of computer vision applications. Then, it allows for the automatic synthesis of the application model into an OpenVX description for the hardware and constraints-aware application tuning. Finally, the methodology allows integrating the OpenVX application with Robot Operating System (ROS), which is the de-facto reference standard for developing robotic software applications. The OpenVX-ROS interface allows cosimulating and parametrizing the application by considering the actual robotic environment and the application reuse in any ROS-compliant system. Experimental results have been conducted with two real case studies: An application for digital image stabilization and the ORB descriptor for simultaneous localization and mapping (SLAM), which have been developed through Simulink and, then, automatically synthesized into OpenVX-VisionWorks code for an NVIDIA Jetson TX2 board.

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Addressing System-Level Optimization with OpenVX Graphs

Cited by 38 publications

References 4 publications

Extending Halide to Improve Software Development for Imaging DSPs

Extending Halide to Improve Software Development for Imaging DSPs

OpenVX-Based Python Framework for Real-time Cross-Platform Acceleration of Embedded Computer Vision Applications

Integrating Simulink, OpenVX, and ROS for Model-Based Design of Embedded Vision Applications

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