A branch and bound method for stochastic global optimization
Mathematical Programming: Series A and B
Bus access optimization for distributed embedded systems based on schedulability analysis
DATE '00 Proceedings of the conference on Design, automation and test in Europe
Reconfigurable computing: a survey of systems and software
ACM Computing Surveys (CSUR)
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
Constraint-Based Scheduling
Constraints-driven scheduling and resource assignment
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Power-aware scheduling of conditional task graphs in real-time multiprocessor systems
Proceedings of the 2003 international symposium on Low power electronics and design
The future of multiprocessor systems-on-chips
Proceedings of the 41st annual Design Automation Conference
Allocation and scheduling of Conditional Task Graphs
Artificial Intelligence
Scheduling conditional task graphs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Multi-stage benders decomposition for optimizing multicore architectures
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
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This paper describes a complete and efficient solution to the stochastic allocation and scheduling for Multi-Processor System-on-Chip (MPSoC). Given a conditional task graph characterizing a target application and a target architecture with alternative memory and computation resources, we compute an allocation and schedule minimizing the expected value of communication cost, being the communication resources one of the major bottlenecks in modern MPSoCs. Our approach is based on the Logic Based Benders decomposition where the stochastic allocation is solved through an Integer Programming solver, while the scheduling problem with conditional activities is faced with Constraint Programming. The two solvers interact through no-goods. The original contributions of the approach appear both in the allocation and in the scheduling part. For the first, we propose an exact analytic formulation of the stochastic objective function based on the task graph analysis, while for the scheduling part we extend the timetable constraint for conditional activities. Experimental results show the effectiveness of the approach.