Instructor: Dr. Phillip Jones CPRE 583 Reconfigurable Computing Lecture 14: Fri 10/13/2010 (Streaming Applications) Instructor: Dr. Phillip Jones (phjones@iastate.edu) Reconfigurable Computing Laboratory Iowa State University Ames, Iowa, USA http://class.ee.iastate.edu/cpre583/

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)

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

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

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

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

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)

Projects: Target Timeline Work on projects: 10/22 - 12/8 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)

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

Common Questions

Common Questions

Common Questions

Overview Steaming Applications (Chapters 8 & 9) Simulink SCORE

What you should learn Two approaches for implementing streaming applications

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

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

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

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

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

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

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

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

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

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

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

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

Simulink: Example Model

Simulink: Example Model

Simulink: Sub-Module

Simulink: Example Model

Simulink: Example Model

Simulink: Example Plot

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 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 50 100 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 50 100 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 50 100 -100 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 50 100 -100 -100 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 50 100 -100 -100 -100 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 50 100 -100 -100 -100 50 150 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 50 100 -100 -100 -100 50 150 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 50 100 -100 -100 -100 50 150 -150 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 50 100 -100 -100 -100 50 150 -150 -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 50 100 -100 -100 -100 50 150 -150 -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 50 100 -100 -100 -100 50 150 -150 -50 -50 50 200

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

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

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

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

Top Level

Shifter

Multiplier

Input Image

Output Image

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 http://brass.cs.berkeley.edu/documents/score_tutorial.html

Next Lecture Data Parallel

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

Lecture Notes