1 / 20

Adaptive Computing in NASA Multi-Spectral Image Processing

ACME LAB. Adaptive Computing in NASA Multi-Spectral Image Processing. Mark L. Chang Northwestern University Evanston, IL mchang@ece.nwu.edu. Scott A. Hauck University of Washington Seattle, WA hauck@ee.washington.edu. Background.

qiana
Download Presentation

Adaptive Computing in NASA Multi-Spectral Image Processing

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. ACME LAB Adaptive Computing in NASAMulti-Spectral Image Processing Mark L. Chang Northwestern University Evanston, IL mchang@ece.nwu.edu Scott A. Hauck University of Washington Seattle, WA hauck@ee.washington.edu 1

  2. Background • (1991) Initiative by NASA to study Earth as an environmental system—Earth Science Enterprise (ESE) • (1999) Launch of the first Earth Observation System (EOS) satellite, Terra 2

  3. L2 L4 L1 L3 Receiver Level 0 Instrument 1 Instrument 2 Instrument 3 Instrument n The Data Flow • EOS divides telemetry processing into five levels with the following flow: 3

  4. MODIS Instrument The Processing Problem • I/O intensive • Terra satellite generates ~918 Gbytes of data per day • Current NASA-supported data holdings total ~125,000 Gbytes • MODIS instrument accounts for over half the daily data and processing load 4

  5. RAM RAM RAM RAM RAM RAM RAM RAM RAM RAM RAM RAM Why Adaptive Computing? • Instrument dependent processing • Data products involve many different algorithms • Algorithms often change over the lifetime of the instrument 5

  6. MATCH Compiler • Current mappings are done by hand • Hardware description languages (Verilog, VHDL) • C program interface to adaptive compute engine • Requires low-level understanding of the architecture • MATCH == MATlab Compiler for Heterogeneous computing systems • MATLAB codes compiled to a configurable computing system automatically • Embedded processors, DSPs, and FPGAs • Performance goals • Within a factor of 2-4 of the best manual approach • Optimize performance under resource constraints 6

  7. MATCH Compiler Framework • Parse MATLAB programs into intermediate representation • Build data and control dependence graph • Identify scopes for fine-grain, medium grain, and coarse grain parallelism • Map operations to multiple FPGAs, multiple embedded processors and multiple DSP processors • Automatic parallelization, scheduling, and mapping 7

  8. MATCH Testbed Development Environment: SUN Solaris 2, HP HPUX and Windows Ultra C/C++ for MVME TI C for TMS320 XILINX XACT for XILINX • Annapolis Wildchild board • Nine XILINX 4010 FPGAs • 2 MB RAM • Wildfire software • Transtech TDMB 428 • DSP board • Four TDM 411 cards containing TI TMS 320C4 DSP, 8 MB RAM • TI C compiler • Motorola MVME-2604 • embedded boards • IBM PowerPC 604 • 64 MB RAM • OS-9 OS • Ultra C compiler Force 5V MicroSPARC CPU 64 MB RAM VME bus and chassis 8

  9. Motivation for MATCH • NASA scientists prefer MATLAB • High-level language, good for prototyping and development • NASA applications are well-suited to the MATCH project • Lots of image and signal processing applications • Same domain as users of embedded systems • High degree of data parallelism • Small degree of task parallelism • NASA has an interest in adaptive technologies (ASDP) • Will be a benchmark for the MATCH compiler 9

  10. Multi-spectral Image Classification • Want to classify a multi-spectral image in order to make it more useful for analysis by humans • Used to determine type of terrain being represented • Similar to data compression • Similar to clustering analysis Pixel[000][000] = ForestPixel[123][123] = UrbanPixel[255][212] = TundraPixel[410][230] = Water etc… 10

  11. Multi-Spectral Classification 11

  12. MATLAB Iterative for p=1:rows*cols % load pixel to process pixel = data( (p-1)*bands+1:p*bands ); class_total = zeros(classes,1); class_sum = zeros(classes,1); % class loop for c=1:classes class_total(c) = 0; class_sum(c) = 0; % weight loop for w=1:bands:pattern_size(c)*bands-bands weight = class(c,w:w+bands-1); class_sum(c) = exp( -(k2(c)*sum( (pixel-weight').^2 ))) + class_sum(c); end class_total(c) = class_sum(c) * k1(c); end results(p) = find( class_total == max( class_total ) )-1; end 12

  13. MATLAB Vectorized % reshape data weights = reshape(class',bands,pattern_size(1),classes); for p=1:rows*cols % load pixel to process pixel = data( (p-1)*bands+1:p*bands); % reshape pixel pixels = reshape(pixel(:,ones(1,patterns)), bands,pattern_size(1),classes); % do calculation vec_res = k1(1).*sum(exp( -(k2(1).*sum((weights-pixels).^2)) )); vec_ans = find(vec_res==max(vec_res))-1; results(p) = vec_ans; end 13

  14. Band K2 Mult Accumulator Pixel Unit Memory K2[K] Weight Memory Memory PNN Square Controller Unit Subtraction PE0 PE1 # of bands times PE2 PE3 PE4 Unit Class Compare exp / K1[K] Class K1[K] Mem Accumulator K1 Mult exp Mult exp LUT # weights/class times Unit Unit Unit Result Memory Initial FPGA Mapping 5% 67% 85% 82% 82% 14

  15. Band K2 Mult Accumulator Pixel Unit Memory K2[K] Weight Memory Memory PNN Square Controller Unit Subtraction PE0 PE1 # of bands times PE2 PE3 PE4 Unit Class Compare exp / K1[K] Class K1[K] Mem Accumulator K1 Mult exp Mult exp LUT # weights/class times Unit Unit Unit Result Memory Improving the Mapping • Improve speed of PNN • Utilize all eight processing elements • Time-multiplex low-rate functions • Vary precision of multipliers/lookups 1:1 1:1 1:4 1:4 1:20 15

  16. Optimized Mapping PE1 Subtract Square PE2 Subtract Square PE0 PE5 PE6 PE7 PE7 Pixel Reader K2 Multiplier Exponent Lookup Class Accumulator K1 Multiplier Class Comparison PE3 Subtract Square 5% 85% 61% 54% 97% PE4 Subtract Square 75% 16

  17. Results Raw Image Data Processed Image Reference: HP C180 Workstation 17

  18. Results (Cont’d) Reference:MATCH Testbed Force 5V MicroSPARC CPU 64 MB RAM 18

  19. Results (Cont’d) 19

  20. Conclusions • NASA is interested in adaptive computing • NASA has many candidate applications • High processing loads and I/O requirements • Applications are well-suited for acceleration using adaptive computing • Scientists will want to write in MATLAB rather than C+VHDL • Good benchmarks for the MATCH compiler • Will help identify functions and procedures necessary for real-world applications 20

More Related