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Content Provider | ACM Digital Library |
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Author | Lanzi, Pier Luca Sciuto, Donatella Ferrandi, Fabrizio Ceriani, Marco Tumeo, Antonino |
Abstract | In this paper, we apply multi-objective evolutionary computation to the synthesis of real-time, embedded, heterogeneous, multiprocessor systems (briefly, Multiprocessor Systems-on-Chip or MP-SoCs). Our approach simultaneously explores the architecture, the mapping and the scheduling of the system, by using multi-objective evolution. In particular, we considered three approaches: a multi-objective genetic algorithm, multi-objective Simulated Annealing, and multi-objective Tabu Search. The algorithms search for optimal architectures, in terms of processing elements (processors and hardware accelerators) and communication infrastructure, and for the best mappings and schedules of multi-rate real-time applications given objectives such as: system area, hard and soft dead-lines violations, dimensions of memory buffers. We formalize the problem, describe our flow and compare the three algorithms, dis- cussing which one performs better with respect to different classes of applications. |
Starting Page | 1267 |
Ending Page | 1274 |
Page Count | 8 |
File Format | |
ISBN | 9781450300728 |
DOI | 10.1145/1830483.1830710 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2010-07-07 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Multi-objective evolution Systems-on-chip synthesis |
Content Type | Text |
Resource Type | Article |
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