11,549 research outputs found
Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device
Currently, most designers face a daunting task to
research different design flows and learn the intricacies of
specific software from various manufacturers in
hardware/software co-design. An urgent need of creating a
scalable hardware/software co-design platform has become a key
strategic element for developing hardware/software integrated
systems. In this paper, we propose a new design flow for building
a scalable co-design platform on FPGA-based system-on-chip.
We employ an integrated approach to implement a histogram
oriented gradients (HOG) and a support vector machine (SVM)
classification on a programmable device for pedestrian tracking.
Not only was hardware resource analysis reported, but the
precision and success rates of pedestrian tracking on nine open
access image data sets are also analysed. Finally, our proposed
design flow can be used for any real-time image processingrelated
products on programmable ZYNQ-based embedded
systems, which benefits from a reduced design time and provide a
scalable solution for embedded image processing products
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FABRIC: A National-Scale Programmable Experimental Network Infrastructure
FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities
Integrated Design and Implementation of Embedded Control Systems with Scilab
Embedded systems are playing an increasingly important role in control
engineering. Despite their popularity, embedded systems are generally subject
to resource constraints and it is therefore difficult to build complex control
systems on embedded platforms. Traditionally, the design and implementation of
control systems are often separated, which causes the development of embedded
control systems to be highly time-consuming and costly. To address these
problems, this paper presents a low-cost, reusable, reconfigurable platform
that enables integrated design and implementation of embedded control systems.
To minimize the cost, free and open source software packages such as Linux and
Scilab are used. Scilab is ported to the embedded ARM-Linux system. The drivers
for interfacing Scilab with several communication protocols including serial,
Ethernet, and Modbus are developed. Experiments are conducted to test the
developed embedded platform. The use of Scilab enables implementation of
complex control algorithms on embedded platforms. With the developed platform,
it is possible to perform all phases of the development cycle of embedded
control systems in a unified environment, thus facilitating the reduction of
development time and cost.Comment: 15 pages, 14 figures; Open Access at
http://www.mdpi.org/sensors/papers/s8095501.pd
Computer Architectures to Close the Loop in Real-time Optimization
© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other
Lessons learned from the design of a mobile multimedia system in the Moby Dick project
Recent advances in wireless networking technology and the exponential development of semiconductor technology have engendered a new paradigm of computing, called personal mobile computing or ubiquitous computing. This offers a vision of the future with a much richer and more exciting set of architecture research challenges than extrapolations of the current desktop architectures. In particular, these devices will have limited battery resources, will handle diverse data types, and will operate in environments that are insecure, dynamic and which vary significantly in time and location. The research performed in the MOBY DICK project is about designing such a mobile multimedia system. This paper discusses the approach made in the MOBY DICK project to solve some of these problems, discusses its contributions, and accesses what was learned from the project
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