By Meikang Qiu
ISBN-10: 1439817650
ISBN-13: 9781439817650
Ubiquitous in today’s consumer-driven society, embedded structures use microprocessors which are hidden in our daily items and designed to accomplish particular projects. powerful use of those embedded structures calls for engineers to be trained in all levels of this attempt, from making plans, layout, and research to production and marketing.
Taking a systems-level process, Real-Time Embedded structures: Optimization, Synthesis, and Networking describes the sector from 3 targeted facets that make up the 3 significant developments in present embedded procedure design.
The first part of the textual content examines optimization in real-time embedded structures. The authors current scheduling algorithms in multi-core embedded platforms, train on a powerful size opposed to the wrong info that could exist in embedded structures, and talk about capability difficulties of heterogeneous optimization. the second one part specializes in synthesis-level ways for embedded structures, together with a scheduling set of rules for part switch reminiscence and scratch pad reminiscence and a remedy of thermal-aware multiprocessor synthesis know-how. the ultimate part seems at networking with a spotlight on activity scheduling in either a instant sensor community and cloud computing. It examines the merging of networking and embedded platforms and the ensuing evolution of a brand new kind of procedure referred to as the cyber actual method (CPS).
Encouraging readers to find how the pc interacts with its surroundings, Real-Time Embedded structures provides a valid creation to the layout, production, advertising, and destiny instructions of this significant tool.
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Additional info for Real-time Embedded Systems: Optimization, Synthesis, and Networking
Sample text
Probabilistic retiming (PR) had been proposed by Tongsima et al. in [207, 153]. , reduce the length of the longest path of the graph such that the probability of the longest path computation time being less than or equal to the given timing constraint, L, is greater than or equal to a given confidence probability P . Since the execution times of the nodes can be either fixed or varied, a probability model is employed to represent the execution time of the tasks. But PR does not model the hard HA problem which focuses on how to obtain the best assignment from different FU types.
The cost of embedded systems may relate to power, reliability, etc. Therefore, an important problem arises: how to assign a proper FU type to each operation of a DSP application such that the requirements can be met and the total cost can be minimized while satisfying timing constraints with a guaranteed confidence probability [183]. Furthermore, we observe that some tasks may not have fixed execution time. Such tasks usually contain conditional instructions and/or operations that could have different execution times for different inputs [207, 247, 92, 91, 90].
143, 142], which gives a better result than traditional retiming. Column “Search Point” represents the number of design points that have been searched. In “Final Solution” column, “uf” denotes the unfolding factor for achieving the iteration period (“Iter. Period”). Column “#A” represents the number of adders. Column “#M” represents the number of multipliers. The minimum configuration (the required adders and multipliers) computed by algorithms are shown in columns “#A” and “#M” in the table. Column “IP” represents iteration period and column “CS” represents code size.
Real-time Embedded Systems: Optimization, Synthesis, and Networking by Meikang Qiu
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