Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
A Systematic Approach to Exploring Embedded System Architectures at Multiple Abstraction Levels
IEEE Transactions on Computers
Program mapping onto network processors by recursive bipartitioning and refining
Proceedings of the 44th annual Design Automation Conference
ADAM: run-time agent-based distributed application mapping for on-chip communication
Proceedings of the 45th annual Design Automation Conference
Proceedings of the conference on Design, automation and test in Europe
User-aware dynamic task allocation in networks-on-chip
Proceedings of the conference on Design, automation and test in Europe
Dynamic task allocation strategies in MPSoC for soft real-time applications
Proceedings of the conference on Design, automation and test in Europe
System-scenario-based design of dynamic embedded systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Proceedings of the Conference on Design, Automation and Test in Europe
Scenario-oriented design for single-chip heterogeneous multiprocessors
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Scenario-based design flow for mapping streaming applications onto on-chip many-core systems
Proceedings of the 2012 international conference on Compilers, architectures and synthesis for embedded systems
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The application workloads in modern MPSoC-based embedded systems are becoming increasingly dynamic. Different applications concurrently execute and contend for resources in such systems which could cause serious changes in the intensity and nature of the workload demands over time. To cope with the dynamism of application workloads at run time and improve the efficiency of the underlying system architecture, this paper presents a novel scenario-based run-time task mapping algorithm. This algorithm combines a static mapping strategy based on workload scenarios and a dynamic mapping strategy to achieve an overall improvement of system efficiency. We evaluated our algorithm using a homogeneous MPSoC system with three real applications. From the results, we found that our algorithm achieves an 11.3% performance improvement and a 13.9% energy saving compared to running the applications without using any run-time mapping algorithm. When comparing our algorithm to three other, well-known run-time mapping algorithms, it is superior to these algorithms in terms of quality of the mappings found while also reducing the overheads compared to most of these algorithms.