763 research outputs found
Control and Embedded Computing: Survey of Research Directions
This paper provides a survey of the role of feedback control in embedded realtimesystems, presented in the context of a new EU/IST Network of Excellence, ARTIST2.The survey highlights recent research efforts and future research directions in the areasof codesign of computer-based control systems, implementation-aware embedded controlsystems, and control of real-time computing systems
Analytical cost metrics: days of future past
2019 Summer.Includes bibliographical references.Future exascale high-performance computing (HPC) systems are expected to be increasingly heterogeneous, consisting of several multi-core CPUs and a large number of accelerators, special-purpose hardware that will increase the computing power of the system in a very energy-efficient way. Specialized, energy-efficient accelerators are also an important component in many diverse systems beyond HPC: gaming machines, general purpose workstations, tablets, phones and other media devices. With Moore's law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency. This work builds analytical cost models for cost metrics such as time, energy, memory access, and silicon area. These models are used to predict the performance of applications, for performance tuning, and chip design. The idea is to work with domain specific accelerators where analytical cost models can be accurately used for performance optimization. The performance optimization problems are formulated as mathematical optimization problems. This work explores the analytical cost modeling and mathematical optimization approach in a few ways. For stencil applications and GPU architectures, the analytical cost models are developed for execution time as well as energy. The models are used for performance tuning over existing architectures, and are coupled with silicon area models of GPU architectures to generate highly efficient architecture configurations. For matrix chain products, analytical closed form solutions for off-chip data movement are built and used to minimize the total data movement cost of a minimum op count tree
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
Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool
Model predictive control (MPC) is an optimization-based strategy for high-performance control that is attracting increasing interest. While MPC requires the online solution of an optimization problem, its ability to handle multivariable systems and constraints makes it a very powerful control strategy specially for MPC of embedded systems, which have an ever increasing amount of sensing and computation capabilities. We argue that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems. The main burden for the implementation of MPC on FPGAs is the challenging design of the necessary algorithms. We outline an approach to achieve a software-supported optimized implementation of MPC on FPGAs using high-level synthesis tools and automatic code generation. The proposed strategy exploits the arithmetic operations necessaries to solve optimization problems to tailor an FPGA design, which allows a tradeoff between energy, memory requirements, cost, and achievable speed. We show the capabilities and the simplicity of use of the proposed methodology on two different examples and illustrate its advantages over a microcontroller implementation
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