Innovative Emerging Computer Technologies

1. Innovative Emerging Computer Technologies Jacob Barhen and Neena Imam Computing and Computational Sciences Directorate Computational Advances for Distributed Sensing

2. Center for Engineering Science Advanced Research Fundamental theoretical, experimental, and computational research Mission: 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

3. Center for Engineering Science Advanced Research Fundamental theoretical, experimental, and computational research • multicore CELL processor • reconfigurable architectures: XtremeDSP FPGA and HyperX • terascale optical core digital devices: EnLight For distributed sensing applications, some of the most promising advances in the computational area build upon the emergence of CESAR sponsors: DARPA, DOE/SC, MDA, NSF, ONR, NAVSEA, other government agencies

4. Technology for petascale computing:The EnLightTM 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 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

5. S1 S2 SN Threat source localization from distributed sensor net Patrol aircraft monitoring GPS-capable sonobuoys • Application • Submerged threat (e.g., submarine in coastal waters) • Compute wavefront TDOAs (time differences of arrival) for each pair of sensors Sensor data acquisition Foundational steps Illustrative example • 10 sonobuoys sensor net • 7 detect a signal • 21 TDOAs only (by symmetry) TDOAs for each pair of sensors Source localization methodologies M1–M3 • M1 • Maximum likelihood • Iterative least squares • M2 • Closed form solution • M3 • Constrained Lagrangian optimization

6. 9 8 7 6 Delay (in Sampling Intervals) 5 4 3 2 1 0 14 14 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21 TDOAs TDOAs Accuracy results TDOA magnitude (in units of sampling intervals) versus sensor pairs (ordered lexicographically) for 7 active sensors Exact (model) results Sensor-inferred results computed using 64-bit floating-point FORTRAN on Intel Xeon Exact (model) results Sensor-inferred results computed using EnLight 64 hardware Noise and interference are taken as Gaussian processes with a varying power level  For SNR >(, Nt),perfect accuracy achieved Nt= 2048 t = 0.08 s Np = 25

7. 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.

8. Matched filter calculation on EnLight-64 hardware Accuracy comparison -30 • Speed-up factor per processor • E 64: 6,826  2 > 13,000 actual hardware • E 256: 56,624  2 > 113,000 simulator MATLABAlpha MATLABAlpha -35 -40 Output of filter #1, dB -45 -50 -55 2000 2200 2400 2600 2800 3000 3200 3400 3800 4000 3600 Range (meters) • Computation parameters • FFT: 80K complex samples  number of filter banks • 33 filter banks: 32 Doppler cells, 1 target echo

9. Computational ChallengeMissile defense requires hyperspectral imagery for target kill assessment and spectral analysis of high-impact scenarios Orbital signatures Exo-atmospheric target characterization Counter- measuresignatures Vehicle separation Plume signatures Chemical releases Target signatures Kill assessment ormiss distance Trajectoryreconstruction Booster tracks Interceptor performance Failure diagnostics Photo documentation FOR Flash radiometry Airborne asset 9 Barhen_FutureSystems_0611

10. Objective FPA Disperser Field stop Reimaging lens Collimator Flash hyperspectral imaging 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. Model • 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) CTIS uses dispersive optics to eliminate scanning

11. Hyperspectral object reconstruction CTIS Toeplitz block structure 256 128 density = 11.3% 600 Mixed expectation Attractor dynamics Sparse conjugate gradient 500 400 300 Reconstruction Error (norm) 200 100 0 0 10 20 30 40 50 60 70 80 90 100 Iterations

12. Contacts Jacob Barhen Center for Engineering Science Advanced Research Computer Science and Mathematics (865) 574-7131 barenj@ornl.gov SIPRNET: barhenj@ornl.doe.sgov.gov Neena Imam Center for Engineering Science and Advanced Research Computer Science and Mathematics (865) 574-8701 Imamn@ornl.gov 12 Barhen_FutureSystems_0611