116 research outputs found
A Framework for the Design and Simulation of Embedded Vision Applications Based on OpenVX and ROS
Customizing computer vision applications for embedded systems is a common and widespread problem in the cyber-physical systems community. Such a customization means parametrizing the algorithm by considering the external environment and mapping the Software application to the heterogeneous Hardware resources by satisfying non-functional constraints like performance, power, and energy consumption. This work presents a framework for the design and simulation of embedded vision applications that integrates the OpenVX standard platform with the Robot Operating System (ROS). The paper shows how the framework has been applied to tune the ORB-SLAM application for an NVIDIA Jetson TX2 board by considering different environment contexts and different design constraints
The MANGO FET-HPC Project: an overview
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we provide an overview of the MANGO project
and its goal. The MANGO project aims at addressing power, performance
and predictability (the PPP space) in future High-Performance Computing
systems. It starts from the fundamental intuition that effective
techniques for all three goals ultimately rely on customization to adapt
the computing resources to reach the desired Quality of Service (QoS).
From this starting point, MANGO will explore different but interrelated
mechanisms at various architectural levels, as well as at the level of
the system software. In particular, to explore a new positioning across
the PPP space, MANGO will investigate system-wide, holistic, proactive
thermal and power management aimed at extreme-scale energy efficiency.The MANGO project starts in October 2015 and is funded by the European Commission under the Horizon 2020 FET-HPC program. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 671668.Flich Cardo, J.; Agosta, G.; Ampletzer, P.; Atienza Alonso, D.; Cilardo, A.; Fornaciari, W.; Kovac, M.... (2015). The MANGO FET-HPC Project: an overview. IEEE Computer Society. https://doi.org/10.1109/CSE.2015.57
A model-based design flow for embedded vision applications on heterogeneous architectures
The ability to gather information from images is straightforward to human, and one of the principal input to understand external world. Computer vision (CV) is the process to extract such knowledge from the visual domain in an algorithmic fashion. The requested computational power to process these information is very high. Until recently, the only feasible way to meet non-functional requirements like performance was to develop custom hardware, which is costly, time-consuming and can not be reused in a general purpose. The recent introduction of low-power and low-cost heterogeneous embedded boards, in which CPUs are combine with heterogeneous accelerators like GPUs, DSPs and FPGAs, can combine the hardware efficiency needed for non-functional requirements with the flexibility of software development. Embedded vision is the term used to identify the application of the aforementioned CV algorithms applied in the embedded field, which usually requires to satisfy, other than functional requirements, also non-functional requirements such as real-time performance, power, and energy efficiency. Rapid prototyping, early algorithm parametrization, testing, and validation of complex embedded video applications for such heterogeneous architectures is a very challenging task. This thesis presents a comprehensive framework that: 1) Is based on a model-based paradigm. Differently from the standard approaches at the state of the art that require designers to manually model the algorithm in any programming language, the proposed approach allows for a rapid prototyping, algorithm validation and parametrization in a model-based design environment (i.e., Matlab/Simulink). The framework relies on a multi-level design and verification flow by which the high-level model is then semi-automatically refined towards the final automatic synthesis into the target hardware device. 2) Relies on a polyglot parallel programming model. The proposed model combines different programming languages and environments such as C/C++, OpenMP, PThreads, OpenVX, OpenCV, and CUDA to best exploit different levels of parallelism while guaranteeing a semi-automatic customization. 3) Optimizes the application performance and energy efficiency through a novel algorithm for the mapping and scheduling of the application 3 tasks on the heterogeneous computing elements of the device. Such an algorithm, called exclusive earliest finish time (XEFT), takes into consideration the possible multiple implementation of tasks for different computing elements (e.g., a task primitive for CPU and an equivalent parallel implementation for GPU). It introduces and takes advantage of the notion of exclusive overlap between primitives to improve the load balancing. This thesis is the result of three years of research activity, during which all the incremental steps made to compose the framework have been tested on real case studie
Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments
[EN] In this work, we adapt a reconfigurable computer system based on FPGA
technologies to OpenCL programming environments. The reconfigurable system
is part of a compute prototype of the MANGO European project that includes 96
FPGAs. To optimize the use and to obtain its maximum performance, it is essential to adapt it to heterogeneous systems programming environments such as
OpenCL, which simplifies its programming. In this work, all the necessary activities for correct implementation of the software and hardware layer required for
its use in OpenCL will be carried out, as well as an evaluation of the performance
obtained and the flexibility offered by the solution provided.
This work has been performed during an internship of 5 months. The internship is linked to an agreement between UPV and UniNa (Università degli Studi
di Napoli Federico II).[ES] En este trabajo se va a realizar la adaptación de un sistema reconfigurable de
cómputo basado en tecnologías de FPGAs hacia entornos de programación en
OpenCL. El sistema reconfigurable forma parte de un prototipo de cálculo del
proyecto Europeo MANGO que incluye 96 FPGAs. Con el fin de optimizar el
uso y de obtener sus máximas prestaciones, se hace imprescindible una adaptación a entornos de programación de sistemas heterogéneos como OpenCL, lo cual
simplifica su programación y uso. En este trabajo se realizarán todas las actividades necesarias para una correcta implementación de la capa software y hardware
necesaria para su uso en OpenCL así como una evaluación de las prestaciones
obtenidas y de la flexibilidad ofrecida por la solución aportada.
Este trabajo se ha llevado a término durante una estancia de cinco meses en
la Universitat Politécnica de Valéncia. Esta estancia está vinculada a un acuerdo
entre la Universitat Politécnica de Valéncia y la Università degli Studi di Napoli
Federico IIRusso, D. (2020). Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments. http://hdl.handle.net/10251/150393TFG
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