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Instructor: Dr. Phillip Jones (phjones@iastate) Reconfigurable Computing Laboratory

ECpE 583 Reconfigurable Computing Lecture 25: Tue 12/2/2008 Design Patterns & Compute Models: Part I. Instructor: Dr. Phillip Jones (phjones@iastate.edu) Reconfigurable Computing Laboratory Iowa State University Ames, Iowa, USA http://www.ece.iastate.edu

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Instructor: Dr. Phillip Jones (phjones@iastate) Reconfigurable Computing Laboratory

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  1. ECpE 583Reconfigurable ComputingLecture 25: Tue 12/2/2008Design Patterns & Compute Models: Part I Instructor: Dr. Phillip Jones (phjones@iastate.edu) Reconfigurable Computing Laboratory Iowa State University Ames, Iowa, USA http://www.ece.iastate.edu http://class.ece.iastate.edu/cpre583 (coming soon) http://www.arl.wustl.edu/~phjones/cpre583 (temporary)

  2. Class Announcements • Updated schedule: • Design Patterns & Compute Models (12/2) • Parallelism (12/4) • Project Presentations (12/9) • Project Presentations (12/11) • Final (Monday 12/15 12-2pm) • Project concerns • Group Presentation schedule announce next class

  3. Outline • Design patterns • Why are they useful? • Examples • Compute models • Why are they useful? • Examples

  4. References • Reconfigurable Computing (2008) [1] • Chapter 5: Compute Models and System Architectures • Scott Hauck, Andre DeHon • Design Patterns for Reconfigurable Computing [2] • Andre DeHon (FCCM 2004) • Type Architectures, Shared Memory, and the Corollary of Modest Potential [3] • Lawrence Snyder: Annual Review of Computer Science (1986)

  5. Outline • Design patterns • Why are they useful? • Examples • Compute models • Why are they useful? • Examples

  6. Design Patterns • Design patterns: are a solution to reoccurring problems.

  7. Reconfigurable Hardware Design • “Building good reconfigurable designs requires an appreciation of the different costs and opportunities inherent in reconfigurable architectures” [2] • “How do we teach programmers and designers to design good reconfigurable applications and systems?” [2] • Traditional approach: • Read lots of papers for different applications • Over time figure out ad-hoc tricks • Better approach?: • Use design patterns to provide a more systematic way of learning how to design • It has been shown in other realms that studying patterns is useful • Object oriented software [93] • Computer Architecture [79]

  8. Common Langue • Provides a means to organize and structure the solution to a problem • Provide a common ground from which to discuss a given design problem • Enables the ability to share solutions in a consistent manner (reuse)

  9. Describing a Design Pattern [2] • 10 attributes suggested by Gamma (Design Patters, 1995) • Name: Standard name • Intent: What problem is being addressed?, How? • Motivation: Why use this pattern • Applicability: When can this pattern be used • Participants: What components make up this pattern • Collaborations: How do components interact • Consequences: Trade-offs • Implementation: How to implement • Known Uses: Real examples of where this pattern has been used. • Related Patterns: Similar patterns, patterns that can be used in conjunction with this pattern, when would you choose a similar pattern instead of this pattern.

  10. Example Design Pattern • Coarse-grain Time-multiplexing • Template Specialization

  11. Coarse-grain Time-Multiplexing M2 M1 M3 A B M1 M2 M1 M2 A B M3 Temp M3 Temp Configuration 1 Configuration 2

  12. Coarse-grain Time-Multiplexing • Name: Coarse-grained Time-Multiplexing • Intent: Enable a design that is too large to fit on a chip all at once to run as multiple subcomponents • Motivation: Method to share limited fixed resources over to implement a design that is too large as a whole.

  13. Coarse-grain Time-Multiplexing • Applicability: • Configuration can be done on large time scale • No feedback loops in computation • Feedback loop only spans the current configuration • Feedback loop is very slow • Participants: • Computational graph • Control algorithm • Collaborations: Control algorithm manages when sub-graphs are loaded onto the device

  14. Coarse-grain Time-Multiplexing • Consequences: Often platforms take millions of cycles to reconfigure • Need an app that will run for 10’s of millions of cycles before needing to reconfigure • May need large buffers to store data during a reconfiguration • Known Uses: • Video processing pipeline [Villasenor] • “Video Communications using Rapidly Reconfigurable Hardware”, Transactions on Circuits and Systems for Video Technology 1995 • Automatic Target Recognition [[Villasenor] • “Configurable Computer Solutions for Automatic Target Recognition”, FCCM 1996

  15. Coarse-grain Time-Multiplexing • Implementation: • Break design into multiple sub graphs that can be configured onto the platform in sequence • Design a controller to orchestrate the configuration sequencing • Take steps to minimize configuration time • Related patterns: • Streaming Data • Queues with Back-pressure

  16. Coarse-grain Time-Multiplexing M2 M1 M3 A B M1 M2 M1 M2 A B M3 Temp M3 Temp Configuration 1 Configuration 2

  17. Template Specialization Empty LUTs A(1) A(0) LUT LUT LUT LUT - - - - - - - - - - - - - - - - C(3) C(2) C(1) C(0) Mult by 3 Mult by 5 A(1) A(1) A(0) A(0) LUT LUT LUT LUT LUT LUT LUT LUT 0 0 0 1 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1 0 3 6 9 0 5 10 15 C(3) C(2) C(1) C(0) C(3) C(2) C(1) C(0)

  18. Template Specialization • Name: Template Specialization • Intent: Reduce the size or time needed for a computation. (note primary difference from Template pattern is this pattern aims to minimize run-time reconfiguration) • Motivation: Use early-bound data and slowly changing data to reduce circuit size and execution time.

  19. Template Specialization • Applicability: When circuit specialization can be adapted quickly • Example: Can treat LUTs as small memories that can be written. No interconnect modifications • Participants: • Template cell: Contains specialization configuration • Template filler: Manages what and how a configuration is written to a Template cell • Collaborations: Template filler manages Template cell

  20. Template Specialization • Consequences: Can not optimize as much as when a circuit is fully specialize for a given instance. Overhead need to allow template to implement several specializations. • Known Uses: • Multiply-by-Constant • String Matching • Implementation: Multiply-by-Constant • Use LUT as memory to store answer • Use controller to update this memory when a different constant should be used.

  21. Template Specialization • Related patterns: • CONSTRUCTOR • EXCEPTION • TEMPLATE

  22. Template Specialization Empty LUTs A(1) A(0) LUT LUT LUT LUT - - - - - - - - - - - - - - - - C(3) C(2) C(1) C(0) Mult by 3 Mult by 5 A(1) A(1) A(0) A(0) LUT LUT LUT LUT LUT LUT LUT LUT 0 0 0 1 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1 0 3 6 9 0 5 10 15 C(3) C(2) C(1) C(0) C(3) C(2) C(1) C(0)

  23. Next Lecture • Compute Models & Parallelism

  24. Questions/Comments/Concerns • Write down • Main point of lecture • One thing that’s still not quite clear • If everything is clear, then give an example of how to apply something from lecture OR

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