Get the latest tech news
Radiation-tolerant ML framework for space
Contribute to r0nlt/Space-Radiation-Tolerant development by creating an account on GitHub.
Sensitivity-based allocation prioritizes critical neural network layers Layer-specific protection levels adjust based on observed error patterns Resource utilization scales with radiation intensity (215%-265% overhead) Maintained 98.5%-100% accuracy from LEO (10⁷ particles/cm²/s) to Jupiter (10¹² particles/cm²/s) Tracks reliability history of each redundant component Applies weighted voting based on observed error patterns Outperformed traditional TMR by 2.3× in high-radiation environments Demonstrated 9.1× SEU mitigation ratio compared to unprotected computation Autonomous Navigation: ML-based navigation systems that maintain accuracy during solar storms or high-radiation zones Onboard Image Processing: Real-time image classification for target identification without Earth communication Fault Prediction: ML models that predict system failures before they occur, even in high-radiation environments Resource Optimization: Intelligent power and thermal management in dynamically changing radiation conditions Science Data Processing: Onboard analysis of collected data to prioritize downlink content
Or read this on Hacker News
