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High-level Synthesis Transformations

High-level Synthesis Transformations. EE202A (Fall 2003): Lecture #9. Note: Several slides in this Lecture are from Prof. Miodrag Potkonjak, UCLA CS. Overview. High Level Synthesis Scheduling Estimations Transformations. Transformations. Goals of High Level Synthesis

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High-level Synthesis Transformations

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  1. High-level SynthesisTransformations EE202A (Fall 2003): Lecture #9 Note: Several slides in this Lecture are fromProf. Miodrag Potkonjak, UCLA CS

  2. Overview • High Level Synthesis • Scheduling • Estimations • Transformations

  3. Transformations • Goals of High Level Synthesis • To translate the specification of the algorithm into efficient hardware implementation • The quality of the implementation depends on how the high level synthesis tasks are performed and on the computational structure of the algorithm • Purpose of Transformations • To improve flexibility of the computational structure for the high level synthesis

  4. Transformations

  5. Commutativity, Associativity, Distributivity

  6. Associativity • Associativity is frequently used for tree height reduction

  7. Temporal Transformations D (a) * D (b) = D (a * b)

  8. Retiming

  9. Retiming (contd.)

  10. How does Retiming help? before after

  11. Example of Transformation (Cont’d) • Maximum number of operation that can be executed in a given cycle

  12. Clock Period Minimization • Example; 100 stage lattice filter • Assume add and multiply take 1 and 2 units of time respectively • Critical path in dotted line • Minimum sample period = 105

  13. Clock Period Minimization (contd.) • 2-slowdown version

  14. Clock Period Minimization (contd.) • Retimed version of the 2-slow down model • Critical path = 6 • Minimum sample period = 12

  15. Retiming for Register Minimization

  16. Example of Retiming for Register Minimization

  17. Unfolding

  18. Unfolding (contd.)

  19. How to do Unfolding?

  20. Another Example

  21. How does Unfolding help? • Unfolding a DFG with iteration bound T results in a J-unfolded DFG with iteration bound JT • Seems like no win, but can help with actual sample period in two scenarios • Case 1: sample period could not be made equal to iteration period because of some node with computation time greater than T • Case 2: sample period could not be made equal to iteration period because T is not an integer

  22. Example of Case 1 T=3 Minimum sample period = 4 T=6 Minimum sample period = 6/2=3

  23. Example of Case 2 T=4/3 Minimum sample period = 2 T=4 Minimum sample period = 4/3

  24. Transformations: Bottleneck Identification

  25. Transformations:Enabling Principle

  26. Transformations:How to Improve Designs?

  27. Transformations for Area Optimization

  28. Transformations: Summary • Application Range • Filters are ideal targets for the transformation, since the delays are present in all filter structures • Any problem that includes loops using the previous samples • Side Effects • Transformations leading to a shorter critical path may or may not lead to the minimization of the implementation area • How can we deal with blocks in finding the minimal path or the minimal hardware implementation

  29. Conclusions • High Level Synthesis • Connects Behavioral Description and Structural Description • Scheduling, Estimations, Transformations • High Level of Abstraction, High Impact on the Final Design

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