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

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

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Data Faults Advanced real-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 the same 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

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst 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)

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Next Generation Space Telescope A deep space telescope 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

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst 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

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst 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 ) j=1 R j where  ini is the probability for initializing a fresh run, and R is the length of the longer run among both directions.

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst 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

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst 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

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Precision Improvement for GCM datasets A promising reduction factor in input average relative error, in the range ~50 to ~1000, is obtained for a practical range  0 <10% Probability of a bitflip in data Relative Error in Dataset (%)

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Computational Overhead Sensitivity  can be adjusted to scale the algorithm to the achieve apposite balance between correction and computational overhead

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Results for correlated input faults The two smoothing algorithms perform very similarly, but Algo_NGST yields better performance across all probabilities by reducing false alarms Probability of a bitflip in data Relative Error in Dataset (%)

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst 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

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst OTIS Datasets Three distinctive datasets from OTIS Blob: Broad areas of unchanging temperature, high correlation Stripe: Prominent vertical turbulence, other regions benign Spots: Plethora of spots, turbulence distributed 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 * Ref: E. Ciocca

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Performance Comparison for “Blob” A very high gain in precision is obtained when bitflips are present in highly correlated data. Probability of a bitflip in data Relative Error in Dataset (%)

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Performance Comparison for “Stripe” Probability of a bitflip in data Relative Error in Dataset (%)

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst Performance Comparison for “Spots” Probability of a bitflip in data Relative Error in Dataset (%)

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst OTIS Results for correlated input faults Beyond a certain limit, the preprocessing is profligate in generating false positives Probability of a bitflip in data Relative Error in Dataset (%)

Architecture and Real-Time Systems Lab – University of Massachusetts, Amherst 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

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