Applications of AI in Space Domain

Abstract

This comprehensive review examines the critical role of Artificial Intelligence in advancing autonomous space missions, spanning real-time data processing, celestial navigation, and on-board decision-making. The study highlights the significant architectural challenges associated with deploying complex AI workloads in harsh radiation environments, necessitating high-performance, low-power edge computing solutions. It concludes that leveraging open-source hardware standards, specifically the RISC-V Instruction Set Architecture, is essential for designing the next generation of resilient and customizable space-grade processors.

Report

Key Highlights

• On-Orbit Autonomy: AI algorithms are shifting processing capabilities from ground stations to satellites, enabling rapid anomaly detection and autonomous maneuver planning crucial for deep space and mega-constellation operations.
• Data Bottleneck Mitigation: On-board ML inference drastically reduces the volume of data transmitted to Earth (downlink), focusing bandwidth only on scientifically relevant information, especially for Earth observation and astronomical missions.
• Hardware Constraints: The primary limiting factors for space AI deployment are radiation tolerance (mitigating Single-Event Upsets), extremely strict power envelopes (often sub-1 Watt for AI cores), and the need for high-throughput computation.
• Open Architecture Preference: The paper advocates strongly for moving away from proprietary architectures toward open standards to facilitate faster innovation, supply chain security, and customization tailored to radiation-hardened designs.

Technical Details

• AI Methodologies: The applications reviewed rely heavily on lightweight Convolutional Neural Networks (CNNs) for image classification and sensor fusion, along with Reinforcement Learning (RL) techniques for adaptive spacecraft control.
• Architecture Focus: The preferred architecture utilizes a heterogeneous compute fabric, typically integrating general-purpose CPUs with custom Tensor Processing Units (TPUs) or specialized vector extensions designed for fixed-point arithmetic.
• Radiation Mitigation: Implementation of Triple Modular Redundancy (TMR) is crucial, applied not just at the software or system level, but increasingly integrated directly into the design of RISC-V core registers and logic units to improve intrinsic fault tolerance.
• RISC-V Customization: The extensibility of RISC-V is crucial for optimizing silicon use. Specific vector extensions (like the 'V' extension) and custom instructions are utilized to accelerate matrix multiplication and convolution operations without incurring the area penalty of massive general-purpose floating-point units.

Implications

• Standardization Driver: The unique demands of space—high reliability, extreme environments, and mandatory long-term support—position the space domain as a critical validator for the robustness and resilience of the RISC-V ecosystem.
• Acceleration of Custom IP: The requirement for specialized AI accelerators that fit tight power and area budgets forces rapid development of RISC-V custom extensions. This benefits the broader tech ecosystem by generating verified, low-power AI processing IP that can be repurposed for terrestrial edge computing.
• Supply Chain Security: As space agencies seek non-ITAR (International Traffic in Arms Regulations) components, the open, transparent, and international nature of the RISC-V ISA provides a critical strategic advantage over legacy proprietary architectures.
• Ecosystem Tooling Maturation: The need for verifiable, radiation-hardened RISC-V implementations drives significant investment in advanced verification tools, compilers (e.g., LLVM backends for specialized extensions), and formal methods, raising the overall quality and maturity of the RISC-V software ecosystem.