Presented by Innovative Emerging Computer Technologies Jacob Barhen and Neena Imam Computing and Computational Sciences Directorate Computational Advances

Presented by

Innovative Emerging Computer Technologies

Jacob Barhen and Neena ImamComputing and Computational Sciences Directorate

Computational Advances for Distributed Sensing

2 Barhen_FutureSystems_0611

CESAR sponsors: DARPA, DOE/SC, MDA, NSF, ONR, NAVSEA, other government agencies

Center for Engineering Science Advanced ResearchFundamental theoretical, experimental, and computational research

Mission: Support DOD and the Intelligence CommunityMission: Support DOD and the Intelligence Community

Examples of current research topics: Missile defense: C2BMC, HALO-2 project, flash hyperspectral imaging

Sensitivity and uncertainty analysis of complex simulation models

Laser array synchronization (directed energy, ultraweak signal detection, communications, terahertz sources)

Terascale computing devices: EnLight optical core processor, IBM multicore CELL BE, field-programmable gate arrays (FPGA)

Nanoscale science, hybrid Nanoelectronics for high-performance computing (HPC)

Anti-submarine warfare: source localization, sensor nets, Doppler-sensitive waveforms, LCCA beamforming, multisensor fusion

Quantum optics applied to cryptography

Computer networks, wireless reconfigurable sensor network


CESAR sponsors: DARPA, DOE/SC, MDA, NSF, ONR, NAVSEA, other government agencies

Center for Engineering Science Advanced Research

multicore CELL processor

reconfigurable architectures: XtremeDSP FPGA and HyperX

terascale optical core digital devices: EnLight

Fundamental theoretical, experimental, and computational research

For distributed sensing applications, some of the most promising advances in the computational area build upon the emergence of


EnLight 64 demonstrator

• Enhanced by on-node FPGA-based processing and control

• Includes leading edge conventional processor to deliver a full functionality

Power dissipation (at 8000 GOPS throughput):•EnLight: 40 W (single board), i.e., 5 mW per Giga MAC

•DSP solution: 2.79 kW (62 boards, 16 DSPs per board), i.e. 352mW per Giga MAC

Technology for petascale computing:The EnLight TM 64 prototype optical core processor

Optical core is prime contributor to the outstanding processing power

Full matrix-vector multiplication per single clock cycle

Fixed point architecture, 8-bit accuracy per clock cycle


Illustrative example•10 sonobuoys sensor net• 7 detect a signal•21 TDOAs only (by symmetry)

M1• Maximum

likelihood• Iterative least

squares

Sensor data acquisition

M2 •Closed form

solution

M3•Constrained

Lagrangian optimization

Foundational steps

Source localization methodologies M1–M3

Patrol aircraft monitoring GPS-capable sonobuoys

Threat source localization from distributed sensor net

Application Submerged threat (e.g., submarine

in coastal waters) Compute wavefront TDOAs (time

differences of arrival) for each pair of sensors

TDOAs for each pair of sensors

S2S1 SN


0

1

2

3

4

5

6

7

8

9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

TDOAs

Del

ay (

in S

amp

lin

g I

nte

rval

s)

20 21

TDOA magnitude (in units of sampling intervals) versus sensor pairs (ordered lexicographically) for 7 active sensors

Noise and interference are taken as Gaussian processes with a varying power level For SNR >(, Nt ), perfect accuracy achievedNt = 2048 t = 0.08 s Np = 25

Accuracy results

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

TDOAs

20 21

Exact (model) resultsSensor-inferred results computed using 64-bit floating-point FORTRAN on Intel Xeon

Exact (model) resultsSensor-inferred results computed using EnLight 64 hardware


Signal processing for active sensor arrays Keys to superior performance of active distributed sensor networks are

Proper waveform selection Accurate signal/system modeling Efficient real-time signal processing via MF bank implementation

Broadband Doppler-sensitive waveforms provide one potential solution for distributed target tracking For wideband signals, the effect of target velocity is no longer approximated as a

simple “shift” in frequency: Doppler effect includes compression/stretching of the transmitted pulse

Demonstrating that this can be done with minimal power consumption will Enable additional capabilities for future remote surveillance and combat systems Provide a building block for other processing-heavy system functions such as

sonars, underwater communications, beamforming of large arrays, etc.


Matched filter calculation on EnLight-64 hardware

Speed-up factor per processor E 64: 6,826 2 > 13,000

actual hardware E 256: 56,624 2 > 113,000

simulator

Accuracy comparison

Hardware Implementation ResultsTime Performance

Intel Dual

Xeon

Enlight

64a

Enlight

256

Specs2 GHz

1 GB RAM

60 MHz

16.67 ns

125 MHz

8ns

FFT 2 32 128

Timing 9,6262 ms 1.41 ms 0.17 ms

Computation parameters FFT: 80K complex

samples number of filter banks

33 filter banks: 32 Doppler cells, 1 target echo

-30

-35

-40

-45

-50

-55

2000 2600 40003200 3400 3600 38002800 300024002200

Range (meters)

Ou

tpu

t o

f fi

lter

#1,

dB

MATLABAlphaMATLABAlpha


Orbital signatures

Kill assessment or

miss distance

Vehicleseparation

Chemicalreleases

Booster tracks

Interceptor performance

Plume signatures

Counter-measure

signatures

Targetsignatures

Photo documentation

Trajectoryreconstruction

Failurediagnostics

Exo-atmospheric

target characterization

FOR

Computational Challenge Missile defense requires hyperspectral imagery for target kill assessment and spectral analysis of high-impact scenarios

Airborne assetFlash radiometry



CTIS uses dispersive optics

to eliminate scanning

Flash hyperspectral imagingModel• Object cube expressed as vector f with N

elements: N = Nx Ny Nλ

• Finite set of measurements, denoted by FPA data vector { gm | m = 1, ..M }, where M is the number of detector elements

• Imaging system described by means of M N sparse matrix H, determined experimentally

Image Reconstruction• To date: mixed expectation ML optimization

• New: CESAR noise-corrected sparse CG

Computation • Nx = Ny ≥ 256, Nλ ≥ 64 N ≥ 4.2 106

• M ≈ 8192 8192 M ≥ 6.7 107

• Need: reconstruction time window ≤5 ms

• Past performance: over 40 m on Intel Xeon (on much smaller object)

CESAR speed-up targets:

• Factor 1,000-10,000 via algorithms

• Factor 200 via CELL hw (single node)

FPAObjective

Field stop

Disperser

Reimaginglens

Collimator

Objective is to collect a set of registered, spectrally contiguous images of a scene’s spatial radiation distribution within the shortest possible data collection time.


Hyperspectral object reconstruction

600

500

400

300

200

100

00 10 20 30 40 50 60 70 80 90 100

Iterations

Rec

on

stru

ctio

n E

rro

r (n

orm

)

Mixed expectationAttractor dynamicsSparse conjugate gradient

CTIS Toeplitz block structure 256 128 density = 11.3%


Contacts

Jacob BarhenCenter for Engineering Science Advanced ResearchComputer Science and Mathematics(865) [email protected]: [email protected]

Neena ImamCenter for Engineering Science and Advanced ResearchComputer Science and Mathematics(865) [email protected]


Documents

Presented by Innovative Emerging Computer Technologies Jacob Barhen and Neena Imam Computing and Computational Sciences Directorate Computational Advances