Enterprise AI Analysis
Optimizing deep learning models for on-orbit deployment through neural architecture search
Advancements in spaceborne edge computing have facilitated the incorporation of Artificial Intelligence (AI)-powered chips into CubeSats, enabling intelligent data handling and enhanced analytical capabilities with greater operational autonomy. This class of satellites faces stringent energy and memory constraints, necessitating lightweight models typically obtained via compression techniques. This paper addresses model compression through Neural Architecture Search (NAS), enabling computational efficiency and balancing accuracy, size, and latency. More specifically, we design an evolutionary-based NAS framework for onboard processing and evaluate it on both burned-area segmentation and classification tasks. The proposed solution jointly optimizes network architecture and deployment for hardware-specific, resource-constrained platforms, with hardware awareness embedded in the optimization loop to tailor network topologies to the target edge computing chip. The resulting models, designed on CubeSat-class hardware-namely the NVIDIA Jetson AGX OrinTM and Intel® MovidiusTM MyriadTM X-exhibit a memory footprint below 1 MB, achieving real-time, high-resolution inference in orbit while outperforming handcrafted baselines in terms of latency (3× faster) and maintaining competitive mean Intersection over Union (mIoU). Furthermore, on the classification benchmark conducted on an NVIDIA A100-SXM, our approach attained an atthew Correlation Coefficient (MCC) of 0.974-substantially outperforming the baseline Efficient Net-lite0 (0.902) while achieving a × 47 speedup. These results highlight the framework's scalability, enabling seamless deployment across the spectrum from resource-constrained edge devices to datacenter-grade accelerators, thereby supporting next-generation on-orbit computing architectures.
Executive Impact: Key Metrics & ROI
This study introduces a hardware-aware NAS framework tailored specifically for satellite-based edge processing constraints. By integrating evolutionary optimization with real-time latency profiling on heterogeneous hardware platforms-NVIDIA Jetson AGX OrinTM and Intel® MovidiusTM MyriadTM X-we have shown that the proposed approach significantly surpasses state-of-the-art handcrafted architectures in terms of the performance-efficiency trade-off. Additionally, we showcase its potential for future cognitive cloud computing infrastructures in space, aligning with emerging initiatives on on-orbit datacenters and AI-enabled space missions. Specifically, on the NVIDIA Jetson AGX OrinTM, our optimal architecture achieved a remarkable mIoU of 0.845 at 168.1 FPS with merely 34.2K parameters, substantially exceeding the efficiency of MobileOne-S0 (0.859 mIoU at 25.4 FPS) and ResNet-18 (0.840 mIoU at 40.4 FPS), both of which surpass millions of parameters. Similarly impressive results were observed on the Intel® MovidiusTM MyriadTM X, where NAS-derived architectures achieved 0.802 mIoU at 11.5 FPS with fewer than 1K parameters, demonstrating extraordinary compactness and robust quantization resilience. Beyond segmentation, the classification task further highlighted the framework's efficiency: on the NVIDIA A100-SXM, our model achieved a MCC of 0.974 at 8555 FPS, compared to the baseline model (EfficientNet-lite0) 0.902 at 187 FPS, representing an ≈+8% accuracy gain and a 47-fold increase in inference throughput. These results, obtained across edge and datacenter-grade platforms, emphasize the scalability and versatility of the proposed methodology.
3X
Faster Inference
0.974
Classification MCC
1MB
Memory Footprint
Deep Analysis & Enterprise Applications
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Neural Architecture Search (NAS)
NAS automates the discovery of optimized network topologies, moving beyond traditional compression to proactively balance model efficiency and predictive capability. This framework integrates hardware-awareness into the optimization loop, explicitly accounting for memory usage, power consumption, quantization-induced performance degradation, and latency constraints. The Genetic Algorithm (GA)-based approach is particularly suited for non-differentiable objectives and fixed hardware constraints, ensuring models are tailored for on-orbit edge deployment.
Hardware-Aware Optimization
The proposed NAS framework embeds hardware awareness directly into the optimization loop. After each training cycle, the training server interfaces with a designated device (e.g., NVIDIA Jetson AGX Orin™ or Intel® Movidius™ Myriad™ X) to collect performance feedback. This allows for joint optimization of predictive accuracy and hardware-specific metrics like inference latency and memory access cost, ensuring the models meet stringent real-time, resource-constrained onboard application requirements.
On-Orbit Edge Computing
Advancements in spaceborne edge computing enable AI integration into CubeSats, providing intelligent data handling and enhanced autonomy. This is crucial for real-time Earth Observation applications that demand sub-hour response times. The framework's ability to produce lightweight models (memory footprint below 1 MB) achieving real-time, high-resolution inference in orbit, even outperforming handcrafted baselines, is a significant step towards scalable and robust onboard intelligence across diverse satellite architectures.
47X
Speedup on NVIDIA A100-SXM Classification
Enterprise Process Flow
Model Generation
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Training
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Hardware Profiling
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Architectural Optimization
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Deployment
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Real-Time Burned Area Segmentation on Jetson AGX Orin™
Our PyNAS model achieved a mIoU of 0.845 at 168.1 FPS, with only 34.2K parameters. This significantly outperforms MobileOne-S0 (0.859 mIoU at 25.4 FPS) and ResNet-18 (0.840 mIoU at 40.4 FPS), which require millions of parameters. This demonstrates the framework's ability to discover highly efficient and accurate architectures for critical on-orbit Earth Observation tasks.
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