1 / 25

Performance Yield-Driven Task Allocation and Scheduling for MPSoCs under Process Variation

Performance Yield-Driven Task Allocation and Scheduling for MPSoCs under Process Variation. Presenter: Lin Huang Lin Huang and Qiang Xu CU hk RE liable computing laboratory (CURE) The Chinese University of Hong Kong. Process Variation Becomes A Serious Concern.

nay
Download Presentation

Performance Yield-Driven Task Allocation and Scheduling for MPSoCs under Process Variation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Performance Yield-Driven Task Allocation and Scheduling for MPSoCs under Process Variation Presenter: Lin Huang Lin Huang and Qiang Xu CUhk REliable computing laboratory (CURE) The Chinese University of Hong Kong

  2. Process Variation Becomes A Serious Concern • The ever-increasing transistor variability • Spatial correlation characteristic

  3. P1 P2 MPSoC Task Graph Task Schedule Task Allocation and Scheduling for MPSoCs • Given • Determine • Process variation affects performance yield

  4. Limitations of Previous Work • Only a few explicitly consider process variation • All assume the task execution time follows Gaussian distribution • In reality, it can be approximated with Gaussian distribution in some instances at best [Sarangi-ieeetsm08]

  5. Limitations of Previous Work • All assume the execution times of multiple tasks are s-independent • This assumption ignores the spatial correlation characteristic of process variation

  6. Limitations of Previous Work • All assume the execution times of multiple tasks are s-independent • This assumption ignores the spatial correlation characteristic of process variation Consider a pair of MPSoCs i, j

  7. Limitations of Previous Work • With correlation, statistical properties of s-independent Gaussian distribution are not applicable

  8. Agenda • Introduction and motivation • Problem formulation • Proposed quasi-static task allocation and scheduling algorithm • Simulated annealing-based initial task scheduling • Clustering-based performance yield enhancement • Experimental results • Conclusion

  9. Initial Task Scheduling • Modified simulated annealing technique • Solution representation • (scheduling order sequence; resource binding sequence) • Example: (τ1, τ3, τ2, τ4, τ5; P1, P2, P1, P1, P2) • Performance yield estimation • Closed-form statistical analysis is extremely difficult

  10. Initial Task Scheduling • Performance yield estimation • Closed-form statistical analysis is extremely difficult • Monte Carlo simulation meet constraint (1) or not (0) schedule i.i.d. samples of MPSoC frequency map

  11. Initial Task Scheduling • Efficiency of Monte Carlo simulation min = 0 N – number of test chips M – number of chips meeting performance constraints max = 0.031 N = 1,000, confidence level = 95%

  12. Performance Yield Enhancement • With the initial task schedule, some chips might cannot meet performance constraints Residual test chips Covered by initial schedule

  13. Performance Yield Enhancement • Iteratively generate additional task schedules • k-mean clustering and objectively task schedule generation Three clusters

  14. Performance Yield Enhancement • Selection criteria generation • Multilayer perceptron • One time effort • Training sample – test chips • Inputs: frequency map • Outputs: meet constraint or not • Sigmoid function

  15. ... … 1.12 0.85 0.97 Task Schedule Selection ... … 0.02 0.96 0.87 • Given an MPSoC product • Frequency map becomes available • Forward propagation through selection criteria network • Schedule selection rule

  16. Experimental Setup • Task graphs are generated by TGFF • Task number: 31 – 152 • Hypothetical MPSoCs • Heterogeneous or homogeneous • Core number: 4 – 8 • Process variation model • Multivariate normal distribution with spatial correlation [Sarangi-ieeetsm08] • The distance pass which the correlation becomes zero = {0.1, 0.5} • The variation = 3.2%

  17. Experimental Results

  18. Experimental Results

  19. Experimental Results

  20. Experimental Results

  21. Experimental Results

  22. Experimental Results 59.3% 40.8% Sinit 36.9%

  23. Experimental Results

  24. Conclusion • We propose a novel quasi-static variation-aware task allocation and scheduling technique for MPSoC designs • Initial task scheduling • Simulated annealing • Monte Carlo simulation • Performance yield enhancement • k-mean clustering • Multilayer perceptron • Experimental results demonstrate the effectiveness

  25. Performance Yield-Driven Task Allocation and Scheduling for MPSoCs under Process Variation Thank you for your attention !

More Related