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Michel Goraczko, Jie Liu (Microsoft Research, Redmond) Dimitrios Lymberopoulos (Yale University) Slobodan Matic (UC Berkeley) Bodhi Priyantha Feng Zhao.

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Presentation on theme: "Michel Goraczko, Jie Liu (Microsoft Research, Redmond) Dimitrios Lymberopoulos (Yale University) Slobodan Matic (UC Berkeley) Bodhi Priyantha Feng Zhao."— Presentation transcript:

1 Michel Goraczko, Jie Liu (Microsoft Research, Redmond) Dimitrios Lymberopoulos (Yale University) Slobodan Matic (UC Berkeley) Bodhi Priyantha Feng Zhao (Microsoft Research, Redmond) Presentation at DAC 2008, Anaheim, CA June 10 th, Energy-Optimal Software Partitioning in Heterogeneous Multiprocessor Embedded Systems

2 Energy Usage in Embedded Applications Low duty cycle monitoring for long battery life High throughput for realtime critical events processing. Mobile devicesPatient monitoringSmart environments

3 Energy Performance Diversity A single processor with DVFS may not be flexible enough. Energy efficiency in embedded processors Non-trivial wake-up latency and energy costs

4 Heterogeneous Multi-Processor Platforms UCLA LEAP PlatformMSR mPlatform

5 Outline Introduction Design Flow Power State Machine ILP Formulation and Optimization A Sound Source Localization Case Study

6 Software Partitioning Problem Given a time sensitive application, allocate software components to different processors to minimize energy consumption without violating timing constraints. Tasks Processor modes Processor modes Timing Analysis Task timing Partitioning Application structure/ requirements Power model Task-Processor-Mode assignments

7 Power State Machines STBY Power: ~0 mW IDLE Power: 0.25mW 60MHz Power: 141 mW 30MHz Power: 72 mW 7.5MHz Power: 20 mW negligible 1.53 mJ 24.5 ms 0.1 mJ 1.4 ms 1.47 mJ 23.8 ms

8 Software Model Directed acyclic graph of tasks Single-rate periodic execution Known release time Known end-to-end deadline Worst case execution time: Pre-assignments

9 ILP: Variables and Objective Core binary variables task-to-processor assignment; task-to-mode assignment; task transition assignment; Core integer variables task start time instances; Derived variables: In order to convert the problem into ILP formulations, need to further introduce auxiliary variables. Objective: minimize total energy per iteration

10 ILP: Constraints A task can only be allocated to one processor and one mode; A processor can only execute one task at any time; Waking up from sleep modes takes time; Processor total utilization should be less than 1; Tasks have dependencies with in an iteration; Tasks have dependencies across iteration boundaries; No task can start before its release time; All tasks should finish by the deadline;

11 S – Audio Sampling FFT – Fast Fourier Transform SC – Noise Estimation & Signal classification HT – Hypothesis Testing VOTE – Sound detection voting Case Study FFT SC VOTE HT Sound Source Localization

12 Hardware Model Power 2.5V 60MHz full speed 3V 6MHz full speed Full speed /2 speed /8 speed201.4 Idle0.25~0 Wake upEnergy (mJ)Time (ms)Energy (mJ)Time (ms) To full speed ~ To 1/8 speed0.11.4~0

13 Task Profiling ProcModeFFT (ms)SC(ms)HT (ms) ARM7 60MHz MHz MHz MSP43 0 6MHz MHz MHz MHz792300

14 Partitioning Results (1) Deadline: 128ms Need 4 MSP430 60MHz Total energy/iteration: 21.7mJ Average power: 166.7mW ARM7 60MHz MSP-4 6MHz MSP-3 6MHz MSP-2 6MHz MSP-1 6MHz HT FFT SC FFT SC FFT SC FFT SC

15 Scheduling Results (2) Deadline: 256ms Need 2 MSP430 30MHz Total energy/iteration: 22.1mJ Average power: 86.4mW ARM 30MHz MSP4 6MHz MSP3 6MHz MSP2 6MHz MSP1 6MHz HT FFT SC FFT SC FFT SC FFT SC

16 Scheduling Results (3) ARM7 7.5MHz MSP4 6MHz MSP3 6MHz MSP2 6MHz MSP1 6MHz 4xFFT HT SC SC Deadline: 1000ms Need 2 MSP MHz Total energy/iteration: 16.2mJ Average power: 16.2mW

17 Conclusion Processor diversities can help energy saving. Wakeup time and energy must be considered in software partitioning. Optimal software partitioning is NP–hard, but can be formulated as an ILP problem.

18 Limitations & Future Work Execution time variations Aperiodic tasks Lightweight heuristics for online scheduling


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