Energy- Aware Time Change Detec4on Using Synthe4c Aperture Radar On High- Performance Heterogeneous Architectures: A DDDAS Approach

Transcription

1 Energy- Aware Time Change Detec4on Using Synthe4c Aperture Radar On High- Performance Heterogeneous Architectures: A DDDAS Approach Sanjay Ranka (PI) Sartaj Sahni (Co- PI) Mark Schmalz (Co- PI) University of Florida Department of CISE Gainesville, FL DDDAS Program PI Mee>ng 27 Jan 2016

2 1. Research Team 2. Technical Objec4ves 3. Programma4c Informa4on 4. Technical Approach & Results Simula>on Reconstruc>on Coherent Change Detec>on Complexity and Error Analysis Energy and Power Reduc>on 5. Ongoing and Future Work 6. Discussion Overview of Presenta4on 2

3 Research Team q Principal Inves>gator Sanjay Ranka, Ph.D. Research Interests: High- Performance Compu>ng Energy- Aware Compu>ng Big Data Analy>cs q Co- PI Sartaj Sahni, Ph.D. Research Interests: High- Performance Compu>ng Data Structures and Algorithms Signal & Image Processing q Co- PI Research Interests: Mark Schmalz, Ph.D, O.D. High- Performance Compu>ng Signal & Image Processing Simula>on, Error Analysis 3

4 Technical Objec4ves q Develop Energy- Efficient Algorithms for Change Detec4on in Video Synthe4c Aperture Radar (SAR) Imagery q Topics of Inves4ga4on Parallel Architectures (CPUs, GPUs, HMPs) Adap4ve Algorithms for Image Tiling Adap4ve Segmenta4on of SAR Pulse Dataset(s) Efficient SAR Image Reconstruc4on o STEEP Constraints: Space, Time, Error, Energy Profile, and Power Consump4on Change Detec4on Coherent or Incoherent? o Effects of Noise and Clu`er o Incomplete Data o Support for Object Detec4on, Segmenta4on and Recogni4on o Complexity, Efficiency (Time, Space, and Energy or Power Consump4on) 4

5 Technical Approach q Overview of Concept Change detec4on accepts two input images as well as a window size, then generates a difference map. DDDAS Approach: Output of change detector is input to algorithm that assigns spa>al resolu>on to image >les. 5

6 Backprojec4on is decomposed along (a) the output image dimension, where each processing device renders a >le using all the pulse data, or (b) where each processing device renders an en>re image using a subset of the pulses. Tiling is crucial to efficient algorithm performance the selec>on of op>mal >le size is done em- pirically. Pulse Data Selec4on is likewise key to re- construc>on algor- ithm efficiency. We prefetch pulses that contribute to each output (>led) pixel. 6

7 q DDDAS Approach Mul4- Resolu4on DDDAS Resolu4on and Scheduling 1. Master decomposes the problem into atoms (set of pulses to be rendered onto one >le of the output image). 2. Resolu4on Controller determines spa>al resolu>on for render- ing each image >le. 3. Master sends each atom to Mul4level Scheduler that balances load for hetero- geneous devices and maintains locality of access for efficiency. 7

8 q DDDAS Approach Intelligent Pulse Selec4on Four sta4c schemes for distribu4ng 100 iden4cal- resolu4on atoms, each with an equivalent pulse set, to 4 homogeneous processing devices. Dynamic range dimension par44oning redistributes pulse data to minimize com- munica>on cost as the sensor's viewing axis moves around the scene. 8

9 1. Simula4on Construct Experimental Pulse Datasets from Digital Eleva>on Maps Precisely Controlled Test Data Controllable Parameters to Facilitate Performance & Error Analysis 2. Reconstruc4on Mul>resolu>on Superposi>on Algorithm Developed at UF Pulse Dataset Segmenta>on and Output Image Tiling for Efficiency 3. Change Detec4on (CD) CD Algorithm Developed at UF Highlights Regions of Interest Isola>on of Regions Containing Moving Vegeta>on (high frequency ST variance) Construc>on of Mul>resolu>on (Pyramidal) Scene Representa>on Iden>fica>on of Future Target Movement Regions (by variance & context) Sta>s>cal ID and Tracking of Targets by Spa>otemporal (ST) Variance 9

10 4. Complexity and Error Analysis (sta%c : compile- >me and dynamic : run- >me) HIDEF VSAR Processing Algorithm Functional Analysis Computational Analysis Algorithm structure Procedure calls Operation mix Execution trace Complexity Work estimate VSAR HIDEF Algorithm Data Structures Data Structure Layout Memory Analysis Error Propagation Analysis Forward analysis Backward analysis I/O Channel Analysis Uncertainty quantification Architectural Constraints Array indexing - row-major - column-major - custom scan Partitioning/Connectivity Interleaving/Caching Bandwidth/Error rate Packet size & overhead Bandwidth / Connectivity Error characteristics Time-Space-Error Cost Analysis 10

11 5. Energy and Power Consump4on Reduc4on Basis: (1) Algorithmic Model + (2) Energy/Power Measurements Mul4resolu4on Structure Facilitates Region Segmenta4on! Non- Target Regions Reconstructed at Low Resolu4on o Example: Foliage and cover regions o Increased computa>onal efficiency o Decreased power consump>on! Probable Target Regions Reconstructed at Higher Resolu4on o Detail preserved to facilitate target recogni>on and tracking o Increases success of change detec>on algorithm applica>on o Target predic>on model (speed, direc>on) predicts range of posi>ons in next frame o Energy and power consump>on models constrain processing strategy for next frame o Result: Data- and processing- directed reduc>on of power consump>on 11

12 q Results: Simula4on Construct Experimental Pulse Datasets from Digital Eleva4on Maps Receiver Pulse Emi`er z Given: Bidirec>onal Reflec>vity Distribu>on Func>on (BRDF) Emiher Intensity I T ζ sr S Received Intensity I r = I BRDF(θ i ) Emiher Track S = (s 1, s 2,, s P ) S = T monostatic SAR Receiver Track T = (t 1, t 2,, t P ) S T bistatic SAR Number of Pulses P Simulated Ground Plane X x y Pulse Data Resolu>on (per pulse) N B bins Resolu>on NxN pixels of Reconstructed Image defined on X Frame Rate F Objec@ve: Construct pulse dataset D having N P pulses of N B bins each F P N B elements 12

13 q Results: Simula4on & Reconstruc4on Example 1. Construct Digital Eleva>on Map 2. Construct Experimental Pulse Datasets from Digital Eleva>on Maps 3. Compute DEM from Pulse Dataset using SAR Reconstruc>on Algorithm Image of DEM Monosta4c SAR Reconstruc4on with Flare Ar4facts Early Simula4on Results (Reconstruc4on) ζ sr = 45 degrees Emiher alt = 7,071m Receiver alt = 106m Lateral separa>on of central receiver from transmiher = 9,850m 600x600 pixel image 13

14 q Results: Preliminary Reconstruc4on Timing 14

15 q Results: Preliminary Reconstruc4on Timing (May 2015, cont d) 28.4 sec / frame ( pixels) GPU exe 4me 15

16 q Results: Current Algorithm Testbed Master Processor: CPU: Intel Xeon clocked at 2.0 GHz. Opera>ng system : Ubuntu LTS SMP Co- Processor: GPU: Nvidia Tesla C2050, 1 GPU processor with 448 CUDA cores, clocked at GHz GPU Memory 6GB RAM clocked at 1.5 GHz Current Reconstruc4on Timing Results (Jan 2016): Single GPU latency of 2.72 sec / frame [ pixels] Speedup (versus May 2015) = 28.4 sec / 2.72 sec = 10.4X 16

17 q Results: Reconstruc4on Timing, One Nvidia Fermi TM GPU Measure Algorithm Performance with & without External Overhead Execu4on + I/O Time, sec TileSz = 4 TileSz = 8 TileSz = 16 TileSz = 32 Result: Op4mal Tile Size = 16 pixels Image Dimension, pixels Execu4on + I/O Time, sec TileSz = 4 TileSz = 8 TileSz = 16 TileSz = sec / frm ( pixels) GPU exe 4me Image Dimension, pixels 17

18 q Results: Reconstruc4on Timing, Four Nvidia Fermi TM GPUs Measure Algorithm Performance with & without Charm++ Charm++ Slower: Why? ms setup >me - - Lacks automa>c data par>>oning that we have - - Our approach op>m- ized for SAR - - Charm performance improves with data size 18

19 q Results: Preliminary Change Detec4on Algorithm 1: 250m x 170m road scene (SAR) 2: Car traverses road from right to lep 3: Mean of 25 previous frames 4: Standard devia>on of 25 previous frames 19

20 q Results: Preliminary Change Detec4on Algorithm (cont d) False Posi4ve due to tree blowing in the wind. 5: Compute z- score per pixel 6: Apply 15x15- pixel window filter 7: Threshold by mean and st- dev 8: Apply algorithm framewise, per >le Increase Efficiency by focusing on hi- resolu>on areas (probable target) and contextual cues (e.g., car on road) 20

21 Preliminary Conclusions & Future Work q Accomplishments ü Improved Reconstruc4on Algorithm > 10X Faster than Previous ü Preliminary Change Detec4on Algorithm Developed & Tested ü SAR Simulator Developed for Precise Control in Test & Analysis ü Publica4ons (prepared in conjunc>on with this project): " Monograph: preliminary version sub- mihed to John Wiley & Sons John Wiley & Sons, Inc. Journal Paper! Mul4sta4c SAR submihed to IEEE Tr. AES (in revision) 21

22 Preliminary Conclusions & Future Work q Future Work (next 12 months) Ø Performance Op4miza4on: Reconstruc>on & Change Detec>on (RCD) Algorithms Ø Improve CD Algorithm Performance: á Detec>on Probability, â False Alarms Ø Enhancement of Mul4resolu4on Structures to Focus Processing for Efficiency o More Accurately Determine Probable Target Regions (High Resolu>on) o Increase False Alarm Rejec>on via Temporal Spectral Filtering (Low Resolu>on) Ø Measure & Improve: RCD Algorithms Energy Profiles and Power Consump>on Ø Enhance SAR/VSAR Simulator with Flare, Interference, & Diffrac>on Effects Ø Simula4on/Test/Analysis of RCD Algorithms for Distributed Networked Embedded Apps 22

23 Discussion 23