Preprocessing Input Data to Augment Fault Tolerance in Space Applications

1. Preprocessing Input Data to Augment Fault Tolerance in Space Applications Jayakrishnan K. Nair Zahava Koren Israel Koren C. Mani Krishna Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst

2. Motivation • Applications in harsh environments • Onboard processing of huge amounts of sensor data in real time • Vital to anticipate and counter faults preemptively • Example: Space systems vulnerable to many faults • Bombardment by charged particles in space • Alpha Particles • Cosmic Rays • Power Glitches and Stray Capacitance effects • Crosstalk at CCD sensors in the detector array of imaging systems

3. Data Faults • Advancedreal-time applications in hostile environments • High likelihood of input data faults • Data faults occur at source, transit from source or while in memory • We focus on input data errors • Re-running the process or a secondary is useless as the input remains thesame • Current schemes can handle process faults well, but not input data faults • Input precision and reliability is vital to good performance • Corruption at input translates to unreliable, imprecise output

4. Proposed Solution – Input Preprocessing • Input data can be preprocessed to detect and dynamically recover from input errors • Use inherent redundancy in natural data and application semantics • Spatial, Spectral and Temporal Correlation • Dynamic Preprocessing algorithms • Application-specific, use domain knowledge on input datasets • Statistically analyze input data to find potential outliers • Use locality modeling of data in space, spectrum and/or time • Use absolute theoretical bounds on natural data • Automatically adjust to changing turbulence in data • Better results with more cohesive datasets • Reduce false alarms (pseudo-corrections)

5. Next Generation Space Telescope • A deep spacetelescope spacecraft to replace Hubble • Detectors sample once every 1000s, exposed to heavy radiation • Limited downlink bandwidth (6 GB/day) -> onboard processing • COTS processors based system -> increased vulnerability • Cosmic rays can corrupt pixel data : these must be cleaned • Multiple readouts during each baseline (N= 64) • Uses this redundancy to identify and recover from transient effects. * Ref: NASA

6. Input Analytical Model • Gaussian Correlation Model (GCM): The difference between consecutive pixel intensities follow a Gaussian distribution (i+1) = (i) + i where (i) are the pristine pixels in a datasets, i is a Gaussian RV with zero mean and standard deviation representative of simulated NGST datasets

7. Fault Models • Uncorrelated model: Bitflips occur independently with a fixed probability, 0 • Correlated model: Block faults affecting contiguous memory regions show a correlated pattern • Correlation in vertical and horizontal directions are considered • Probability corr ()increases with length R of run of bitflips at  corr () =  (ini) R j j=1 where ini is the probability for initializing a fresh run, and R is the length of the longer run among both directions.

8. Algo_NGST for Dynamic Preprocessing • Application-specific for NGST, uses temporal correlation • Dynamic Statistical Analysis to obtain a voter matrix • Pixels are paired with immediate neighbors at front and back in a pixel-window of width  for least mean distance • Indices of the turbulence across data are obtained • Filter out voters based on sensitivity parameter [1,100] • For trading-off effectiveness with computational overhead • Identify three Bit Windows using dynamic bitmasks • Window A is the most stable bit-window, has MSBs • Window C has LSBs that change with every pixel, hence ignored • Window B in middle has a temporal model for bitwise consistency

9. Image Smoothing Algorithms • Optimal Median Smoothing • Each pixel is replaced by the median of a sliding window • More robust than mean smoothing • Bitwise Majority Voting • Each bit in pixel is replaced by a majority vote in the corresponding bit position in a sliding window • Preserves bit-wise information at the uncorrupted bits

10. Precision Improvement for GCM datasets Relative Error in Dataset (%) Probability of a bitflip in data A promising reduction factor in input average relative error, in the range ~50 to ~1000, is obtained for a practical range 0<10%

11. Computational Overhead Sensitivity can be adjusted to scale the algorithm to the achieve apposite balance between correction and computational overhead

12. Results for correlated input faults Relative Error in Dataset (%) Probability of a bitflip in data The two smoothing algorithms perform very similarly, but Algo_NGST yields better performance across all probabilities by reducing false alarms

13. Orbital Thermal Imaging Spectroscope • OTIS • Reads radiation reflected by earth’s surface for various wavelengths • Computes emissivity and temperature for each coordinate • Input and Output are represented as three-dimensional floating-point arrays • Unlike NGST, there is no temporal redundancy • Spectral Correlation – unreliable as it falls sharply outside a band • Spatial Correlation with Locality bounds – usable for preprocessing

14. OTIS Datasets * Ref: E. Ciocca • Three distinctive datasets from OTIS • Blob: Broad areas of unchanging temperature, high correlation • Stripe: Prominent vertical turbulence, other regions benign • Spots: Plethora of spots, turbulencedistributed over entire region • Assumptions for Preprocessing • Exceptions occur as trends, never as single outliers • Single-bit anomalies are faults • Any theoretically out-of-bound value is a fault

15. Performance Comparison for “Blob” 80 60 40 20 Relative Error in Dataset (%) 0 0.02 0.04 0.06 0.08 0.1 Probability of a bitflip in data A very high gain in precision is obtained when bitflips are present in highly correlated data.

16. Performance Comparison for “Stripe” 80 60 40 20 Relative Error in Dataset (%) 0 0.02 0.04 0.06 0.08 0.1 Probability of a bitflip in data

17. Performance Comparison for “Spots” 80 60 40 20 Relative Error in Dataset (%) 0 0.02 0.04 0.06 0.08 0.1 Probability of a bitflip in data

18. OTIS Results for correlated input faults 80 60 40 20 Relative Error in Dataset (%) 0 0.05 0.01 0.15 0.2 0.25 Probability of a bitflip in data Beyond a certain limit, the preprocessing is profligate in generating false positives

19. Conclusions • Input Faults in Space systems • Process-fault tolerance schemes cannot handle input faults • Input Preprocessing • Inherent Redundancy at input for proactive error correction • Natural Correlation in Temporal, Spectral or Spatial locality • Application-specific preprocessing algorithms for dynamic recovery • Use application semantics and domain knowledge of input data • Results • Works well for uncorrelated and correlated faults • Significant improvements in input precision for varying fault probabilities and statistically diverse datasets

20. Thank You URL: http://www.ecs.umass.edu/ece/realtime Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst