1. Instructor: Dr. Phillip Jones
CPRE 583 Reconfigurable Computing Lecture 14: Fri 10/13/2010 (Streaming Applications)
Instructor: Dr. Phillip Jones
Reconfigurable Computing Laboratory
Iowa State University
Ames, Iowa, USA

2. Announcements/Reminders
Midterm:
Take home portion (40%) given Friday 10/22, due Tue 10/26 (midnight)
In class portion (60%) Wed 10/27
Distance students will have in class portion given via a timed WebCT (2 hour) session (take on Wed, Thur or Friday).
Start thinking of class projects and forming teams
Submit teams and project ideas: Mon 10/11 midnight
Project proposal presentations: Fri 10/22
MP3: PowerPC Coprocessor offload (today/tomorrow)
Problem 2 of HW 2 (released after MP3 gets released)

3. Initial Project Proposal Slides (5-10 slides)
Project team list: Name, Responsibility (who is project leader)
Team size: 3-4 (5 case-by-case)
Project idea
Motivation (why is this interesting, useful)
What will be the end result
High-level picture of final product
High-level Plan
Break project into mile stones
Provide initial schedule: I would initially schedule aggressively to have project complete by Thanksgiving. Issues will pop up to cause the schedule to slip.
System block diagrams
High-level algorithms (if any)
Concerns
Implementation
Conceptual
Research papers related to you project idea

4. Projects Ideas: Relevant conferences
FPL
FPT
FCCM
FPGA
DAC
ICCAD
Reconfig
RTSS
RTAS
ISCA
Micro
Super Computing
HPCA
IPDPS

5. Initial Project Proposal Slides (5-10 slides)
Project team list: Name, Responsibility (who is project leader)
Project idea
Motivation (why is this interesting, useful)
What will be the end result
High-level picture of final product
High-level Plan
Break project into mile stones
Provide initial schedule: I would initially schedule aggressively to have project complete by Thanksgiving. Issues will pop up to cause the schedule to slip.
System block diagrams
High-level algorithms (if any)
Concerns
Implementation
Conceptual
Research papers related to you project idea

6. Weekly Project Updates
The current state of your project write up
Even in the early stages of the project you should be able to write a rough draft of the Introduction and Motivation section
The current state of your Final Presentation
Your Initial Project proposal presentation (Due Fri 10/22). Should make for a starting point for you Final presentation
What things are work & not working
What roadblocks are you running into

7. Projects: Target Timeline
Teams Formed and Idea: Mon 10/11
Project idea in Power Point 3-5 slides
Motivation (why is this interesting, useful)
What will be the end result
High-level picture of final product
Project team list: Name, Responsibility
High-level Plan/Proposal: Fri 10/22
Power Point 5-10 slides
System block diagrams
High-level algorithms (if any)
Concerns
Implementation
Conceptual
Related research papers (if any)

8. Projects: Target Timeline
Work on projects: 10/ /8
Weekly update reports
More information on updates will be given
Presentations: Last Wed/Fri of class
Present / Demo what is done at this point
15-20 minutes (depends on number of projects)
Final write up and Software/Hardware turned in: Day of final (TBD)

9. Project Grading Breakdown
50% Final Project Demo
30% Final Project Report
30% of your project report grade will come from your 5-6 project updates. Friday’s midnight
20% Final Project Presentation

10. Common Questions

11. Common Questions

12. Common Questions

13. Overview
Steaming Applications (Chapters 8 & 9)
Simulink
SCORE

14. What you should learn
Two approaches for implementing streaming applications

15. Data Flow: Quick Review
Graph of operators that data (tokens) flows through
Composition of functions X X +

16. Data Flow: Quick Review
Graph of operators that data (tokens) flows through
Composition of functions X X +

17. Data Flow: Quick Review
Graph of operators that data (tokens) flows through
Composition of functions X X +

18. Data Flow: Quick Review
Graph of operators that data (tokens) flows through
Composition of functions X X +

19. Data Flow: Quick Review
Graph of operators that data (tokens) flows through
Composition of functions X X +

20. Data Flow: Quick Review
Graph of operators that data (tokens) flows through
Composition of functions X X +

21. Data Flow: Quick Review
Graph of operators that data (tokens) flows through
Composition of functions X X +

22. Data Flow Graph of operators that data (tokens) flows through
Composition of functions
Captures:
Parallelism
Dependences
Communication X X +

23. Streaming Application Examples
Some images processing algorithms
Edge Detection
Image Recognition
Image Compression (JPEG)
Network data processing
String Matching (your MP2 assignment)
Sorting??

24. Sorting Initial list of items Split Split Split Sort Sort Sort Sort
merge
merge
merge

25. Example Tools for Streaming Application Design
Simulink from Matlab: Graphical based
SCORE (Steam Computation Organized for Reconfigurable Hardware): A programming model

26. Simulink (MatLab) What is it?
MatLab module that allows building and simulating systems through a GUI interface

27. Simulink: Example Model

28. Simulink: Example Model

29. Simulink: Sub-Module

30. Simulink: Example Model

31. Simulink: Example Model

32. Simulink: Example Plot

33. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection

34. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 1 2 1 -2 2 -1 1 -1 -2 -1
Sobel X gradient
Sobel Y gradient

35. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection
Detect Horizontal Edges
Detect Vertical Edges -1 1 1 2 1 -2 2 -1 1 -1 -2 -1
Sobel X gradient
Sobel Y gradient

36. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 -2 2 50 -1 1 50 50 50 50 50 50 50 50

37. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50 -2 2 50 -1 1 50 50 50 50 50 50 50 50

38. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50 -2 2 50 -1 1 50 50 50 50 50 50 50 50

39. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50 -1 1 50 50 50 50 50 50 50 50

40. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50 -1 1 50 50 50 50 50 50 50 50

41. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50 -1 1 50 50 50 50 50 50 50 50

42. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150 -1 1 50 50 50 50 50 50 50 50

43. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150 -1 1 50 50 50 50 50 50 50 50

44. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50 50 50 50

45. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50 50 50 50

46. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50 50 50 50

47. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150 50 50 50

48. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150 50 50 50

49. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150 50 50 50

50. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50 50 50 50

51. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50 50 50

52. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100 50 50

53. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100 50 50

54. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100 50 50

55. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100 50 50

56. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50 50

57. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150 50

58. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150 50

59. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150
-150 50

60. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150
-150
-50 50

61. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150
-150
-50
-50 50

62. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150
-150
-50
-50 50
200

63. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150
-150
-50
-50 50
200

64. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150
-150
-50
-50 50
200
-200

65. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
150
-150
-50
-50 50
200
-200

66. Example Edge Detection: Sobel
CPRE584 student implementation of Sobel
Basic Sobel Algorithm for Edge Detection -1 1 50 50
-50 -2 2 50
150
-150 -1 1 50 50 50 50 50
150
-150
-50
-50 50
100
-100
-100
-100 50
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-150
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67. Top Level

68. Shifter

69. Multiplier

70. Input Image

71. Output Image

72. SCORE Overview of the SCORE programming approach Developed by
Stream Computations Organized for Reconfigurable Execution
Developed by
University of California Berkeley
California Institute of Technology
FPL 2000 overview presentation

73. Next Lecture
Data Parallel

74. 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

75. Lecture Notes