1. Energy Aware Task Mapping Algorithm For Heterogeneous MPSoC Based Architectures Amr M. A. Hussien¹, Ahmed M. Eltawil¹, Rahul Amin 2 and Jim Martin 2 ¹Wireless Systems and Circuits Laboratory, University of California, Irvine, CA 2 Clemson University, Clemson, SC Problem Definition System Model (cont.)  Estimation is improved by iterations  Heuristic performance is within 20% of the CPLEX BQP optimizer  Several orders of magnitude speedup in execution time Results Proposed heuristic  Future wireless communications aim to provide universal coverage in order to support different heterogeneous networks.  Energy management for software defined radio remains a daunting challenge.  Among several target platforms, MPSoC based architectures have evolved as a compromise between the performance and flexibility requirements.  Task allocation in MPSoCs highly affects the overall energy consumption.  In this work, we consider the MPSoC static task allocation problem for probabilistic applications in order to minimize total average energy consumption based on : Static, Dynamic, Reconfiguration, and Communication energy components  Architecture model o Set of PU types P, each has computational resources λ j, each task t i has certain computational requirements γ i, each task t i is related to the PU type P j by utilization o Static energy : o Dynamic energy : o Reconfiguration energy : o Communication energy :  Problem formulation o Find the optimal mapping M i,j,k of t i on PU instance o Minimize s.t.  Each task is mapped to one and only one instance  Utilization condition is satisfied for each instance in each scenario. o The problem represents constrained binary quadratic programming (CBQP) problem. System model  Application model o Set of scenarios S, set of tasks T o Each has an execution probability χ m o Each comprises set of tasks T m o Each is represented by a task graph o Number of transactions between two tasks t i and t l in S m is defined by σ j is static power of P j τ m is the execution time of S m δ j is the max dynamic power of P j τ i,j is the execution time of S m η i,j is the reconfiguration power of mapping t i on P j is the corresponding reconfiguration time E j,k,q,v is the comm. energy per transaction between the two instances, We propose a heuristic algorithm that : 1.Finds the static mapping between tasks and PU instances. 2.Finds the required number of instances of each PU type. For each task, determine the PU type P j that minimizes the expected energy based on static, dynamic, reconfiguration, and communication energy (No communication energy estimation in the 1 st iteration) Determine the set of tasks T j that has the minimum preliminary energy consumption on T j For each PU type P j, sort the corresponding tasks in T j based on the utilization in descending order Update estimated energy costs based on all energy components on each possible PU instance Assign each task to the corresponding feasible PU instance that has the minimum average energy consumption More tasks in T j More iterations? Output mapping result Yes No  Generated random task graphs for 3 scenarios, 8 tasks  Random variables are used for power and exec. / reconfig. time values  Number of nodes of CPLEX BQP optimizer = 800000 t1t1 t2t2 t3t3 t4t4 t5t5 R R R R R R R R R PU3 PU6 PU9 PU2 PU5 PU8 PU1 PU4 PU7 t1t1 t2t2 t3t3 t4t4 t5t5 Carrier 2: WiFi Carrier 1: WiFi Carrier 2: WiFi Carrier 1: WiMAX Carrier 2: LTE Carrier 2: HSPA Carrier 1: EVDO