Radiation-tolerant ML framework for space
a year ago
- #radiation-tolerance
- #machine-learning
- #space-computing
- The framework is a C++ implementation for machine learning models operating in radiation environments like space.
- It addresses radiation-induced errors such as Single Event Upset (SEU), Multiple Bit Upset (MBU), and others.
- Includes protection mechanisms like Triple Modular Redundancy (TMR) with variants like Basic TMR and Enhanced TMR.
- Provides a quick start guide and API usage examples for implementing radiation protection in ML operations.
- Features dynamic resource allocation, health-weighted voting, and multi-scale temporal protection.
- Validated against NASA and ESA standards, with benchmarks showing high radiation tolerance.
- Architecture includes memory, redundancy, error management, and application layers for defense-in-depth.
- Supports various space environments like LEO, GEO, and Jupiter with adaptive protection levels.
- Case studies demonstrate effectiveness in simulated missions like Europa lander and Mars rover scenarios.
- Current limitations include hardware dependency and computational overhead.
- Future research directions include hardware co-design, dynamic adaptation, and quantum error correction.
- Potential applications include satellite image processing, space exploration, and nuclear facilities.
- Licensed under MIT, with contributions welcome in areas like new TMR strategies and environment models.