Customer-Aware Task Allocation and Scheduling for Multi-Mode MPSoCs

1. Customer-Aware Task Allocation and Scheduling for Multi-Mode MPSoCs Lin Huang, Rong Ye and QiangXu CHhkREliablecomputing laboratory (CURE) The Chinese University of Hong Kong

2. T0 Task Graph T1 MPSoC Platform T2 P1 P2 T3 T4 P1 Periodical Schedule T2 T4 T0 T1 T3 P2 TAS and Execution Modes • Task Allocation and Scheduling • Multi-Mode MPSoCs(multiple execution modes) • Communication service • Audio/Video player • Digital camera… Allocation & Scheduling

3. Personalized TAS • Prior Works [Huang etc., DATE’09, DATE’10] • TAS solutions are generated at design stage • A unified task schedule for each execution mode is constructed for all the products • Usage Strategy Deviation • The products, bought by different end users, experience different life stories. • Personalized TAS solution for each individual product can be more energy-efficient and/or reliable

4. Motivational Example • Consider • A simple MPSoC product with 3 execution modes and 2 processor cores • 10,000 sample products

5. Problem Formulation • Problem 1 [Design Stage] • Given • q execution modes and a directed acyclic task graph for each mode; • The joint probability density function; • A platform-based MPSoC embedded system; • Execution time table; • Power consumption table; • The target service life and the corresponding reliability requirement. • To determine a periodical task schedule for each execution mode, such that the expected energy consumption over all products is minimized under the performance and reliability constraints • Problem 2 [Online Adjustment] • Given • Interval length; • Usage strategy of a specific interval; • Task mapping flexibility constraints. • To achieve the same optimization as Problem 1

6. Proposed TAS at Design Stage • Simulated annealing-based algorithm to minimize the expected energy consumption over all the products • Solution representation • Two kinds of moves • M1: Insert a task in the front of its sink, if no precedunce constraint between them • M2: Change the resource assignment of a task • Cost function Task Graph Task Schedule Zone Representation

7. Proposed Online Adjustment • Overall flow • Resort to similar technique as design stage; • The main difference stays in particularly in the cost function. • Since aging effect is a slow process, online adjustment is performed at regular intervals in range of days or months as a special task. • Analytical model • A forgetful scheme to infer future usage strategy • System reliability is given by

8. Experimental Results • Without mapping constraints Initial Solution Online Adjustment

9. Experimental Results • With mapping constraints Online Adjustment (25% tasks with constraints) Online Adjustment (50% tasks with constraints)

10. Conclusion • Customer-aware TAS on multi-mode MPSoCs • Two phases of proposed approach • Simulated annealing-based algorithm at design stage • Usage-specific online adjustment • Experimental results • Based on hypothetical MPSoCs with various task graphs; • Show the capability to significantly increase the lifetime reliability and energy reduction of MPSoC products. Welcome to visit our poster!