Relevance of radiation-hardened processors for onboard AI
Onboard AI inference reduces downlink needs and enables autonomous decision making in satellites and planetary probes, but space radiation and constrained power budgets force different architecture choices than terrestrial edge devices. Karl A. LaBel NASA Goddard Space Flight Center documents how total ionizing dose and single-event effects shape component selection, showing that architecture choice is driven as much by reliability and mitigation capability as by raw performance. Mission risk tolerance and operational lifetime determine which trade-offs are acceptable.
CPU-based heritage architectures
Radiation-hardened CPUs such as the RAD750 from BAE Systems and SPARC-based LEON cores from Gaisler Research and Cobham have long flight heritage and are favored where determinism, software portability, and proven qualification matter. Heritage CPUs simplify certification and operations for critical control tasks and modest AI workloads, but they often lack the parallel throughput and energy efficiency of modern accelerators. The consequence is that missions prioritizing long-term reliability or using established flight software ecosystems continue to rely on these architectures while accepting limits on complex neural models.
FPGA and heterogeneous SoC accelerators
Radiation-tolerant FPGAs and SoCs provide a strong middle ground for onboard inference because they offer parallelism, reconfigurability, and hardware-accelerated DSP blocks. Microchip Technology describes RTG4 devices aimed at space applications, and vendors such as Xilinx historically provided space-grade programmable devices that enable custom neural-network implementations with error mitigation techniques like triple modular redundancy and built-in ECC. These architectures support larger models and lower-latency inference than heritage CPUs, but they raise development complexity and require detailed verification to meet mission assurance standards. For many Earth-observation and communications missions this balance is the practical choice.
ASICs, neuromorphic research, and future directions
Application-specific ASICs and neuromorphic approaches promise the best power-per-inference but demand high upfront cost and long qualification cycles. European Space Agency research programs and academic groups are exploring radiation-resilient ASIC designs and alternative computing paradigms to reduce energy use for inference in deep-space scenarios. The consequence is a slower adoption curve but potential long-term gains for high-volume constellations or missions with stringent power limits.
Choosing the best architecture depends on mission constraints: heritage CPUs for maximum assurance, FPGAs/heterogeneous SoCs for practical high-throughput inference with mitigations, and ASICs or neuromorphic solutions where power and scale justify development. Across all options, radiation-aware design practices documented by Karl A. LaBel NASA Goddard Space Flight Center and vendor qualification information from BAE Systems Microchip Technology and Gaisler Research remain essential for mission success.