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FPGA Neural Network Accelerators for Space
This episode explores a survey of FPGA-based neural network accelerators for space applications, focusing on what the literature actually demonstrates rather than treating “space AI” as a single vague category. It explains why FPGAs are appealing for onboard inference, including tight control over hardware, energy efficiency, and the ability to support vision, autonomy, compression, navigation, and selective downlink under severe space constraints like limited bandwidth, latency, power, and thermal limits. The discussion also emphasizes a central argument of the paper: evidence for true space-ready systems is thinner than the hype suggests, with a need to distinguish lab demos on commercial boards from hardware that can handle radiation and mission-critical fault tolerance. Listeners would find it interesting because it connects modern AI hardware design to the harsh realities of spacecraft engineering and shows how a careful survey can reveal where the field is mature, where it is overstating its progress, and what technical gaps still matter most.
Sources:
1. FPGA-Based Neural Network Accelerators for Space Applications: A Survey — Pedro Antunes, Artur Podobas, 2025
http://arxiv.org/abs/2504.16173
2. An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study — Emilio Rapuano, Gabriele Meoni, Tommaso Pacini, Gianmarco Dinelli, Gianluca Furano, Gianluca Giuffrida, Luca Fanucci, 2021
https://scholar.google.com/scholar?q=An+FPGA-Based+Hardware+Accelerator+for+CNNs+Inference+on+Board+Satellites%3A+Benchmarking+with+Myriad+2-Based+Solution+for+the+CloudScout+Case+Study
3. Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications — Sebastian Sabogal, Alan George, Gary Crum, 2021
https://scholar.google.com/scholar?q=Reconfigurable+Framework+for+Resilient+Semantic+Segmentation+for+Space+Applications
4. Systematic Reliability Evaluation of FPGA Implemented CNN Accelerators — Zhen Gao, Shihui Gao, Yi Yao, Qiang Liu, Shulin Zeng, Guangjun Ge, Yu Wang, Anees Ullah, Pedro Reviriego, 2023
https://scholar.google.com/scholar?q=Systematic+Reliability+Evaluation+of+FPGA+Implemented+CNN+Accelerators
5. Online continual streaming learning for embedded space applications — Van-Tam Nguyen, Alaa Mazouz, 2024
https://scholar.google.com/scholar?q=Online+continual+streaming+learning+for+embedded+space+applications
6. A Quarter of a Century of Neuromorphic Architectures on FPGAs -- an Overview — Wiktor J. Szczerek, Artur Podobas, 2025
https://scholar.google.com/scholar?q=A+Quarter+of+a+Century+of+Neuromorphic+Architectures+on+FPGAs+--+an+Overview
7. Hardware platforms enabling edge AI for space applications: A critical review — unknown, approximate recent review authors, 2024 or 2025
https://scholar.google.com/scholar?q=Hardware+platforms+enabling+edge+AI+for+space+applications%3A+A+critical+review
8. Study of Radiation Effects on FPGA and GPU based Neural Networks Accelerator Designs — unknown, approximate recent systems authors, 2024 or 2025
https://scholar.google.com/scholar?q=Study+of+Radiation+Effects+on+FPGA+and+GPU+based+Neural+Networks+Accelerator+Designs
9. A Radiation-Hardened Neuromorphic Imager with Self-Healing Spiking Pixels and Unified Spiking Neural Network for Space Robotics — unknown, approximate neuromorphic hardware authors, 2024 or 2025
https://scholar.google.com/scholar?q=A+Radiation-Hardened+Neuromorphic+Imager+with+Self-Healing+Spiking+Pixels+and+Unified+Spiking+Neural+Network+for+Space+Robotics
10. Onboard Optimization and Learning: A Survey — unknown, approximate recent survey authors, 2024 or 2025
https://scholar.google.com/scholar?q=Onboard+Optimization+and+Learning%3A+A+Survey
11. Review on hardware devices and software techniques enabling neural network inference onboard satellites — unknown, approximate recent review authors, 2024 or 2025
https://scholar.google.com/scholar?q=Review+on+hardware+devices+and+software+techniques+enabling+neural+network+inference+onboard+satellites
12. AI Post Transformers: FlatAttention for Tile-Based Accelerator Inference — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-flatattention-for-tile-based-accelerator-56e6ca.mp3
13. AI Post Transformers: AWQ: On-Device LLM Compression and Acceleration — Hal Turing & Dr. Ada Shannon, 2025
https://podcast.do-not-panic.com/episodes/awq-on-device-llm-compression-and-acceleration/
14. AI Post Transformers: KVSwap for Disk-Aware Long-Context On-Device Inference — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-16-kvswap-for-disk-aware-long-context-on-de-f3c15e.mp3
Interactive Visualization: FPGA Neural Network Accelerators for Space
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By mcgrofThis episode explores a survey of FPGA-based neural network accelerators for space applications, focusing on what the literature actually demonstrates rather than treating “space AI” as a single vague category. It explains why FPGAs are appealing for onboard inference, including tight control over hardware, energy efficiency, and the ability to support vision, autonomy, compression, navigation, and selective downlink under severe space constraints like limited bandwidth, latency, power, and thermal limits. The discussion also emphasizes a central argument of the paper: evidence for true space-ready systems is thinner than the hype suggests, with a need to distinguish lab demos on commercial boards from hardware that can handle radiation and mission-critical fault tolerance. Listeners would find it interesting because it connects modern AI hardware design to the harsh realities of spacecraft engineering and shows how a careful survey can reveal where the field is mature, where it is overstating its progress, and what technical gaps still matter most.
Sources:
1. FPGA-Based Neural Network Accelerators for Space Applications: A Survey — Pedro Antunes, Artur Podobas, 2025
http://arxiv.org/abs/2504.16173
2. An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study — Emilio Rapuano, Gabriele Meoni, Tommaso Pacini, Gianmarco Dinelli, Gianluca Furano, Gianluca Giuffrida, Luca Fanucci, 2021
https://scholar.google.com/scholar?q=An+FPGA-Based+Hardware+Accelerator+for+CNNs+Inference+on+Board+Satellites%3A+Benchmarking+with+Myriad+2-Based+Solution+for+the+CloudScout+Case+Study
3. Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications — Sebastian Sabogal, Alan George, Gary Crum, 2021
https://scholar.google.com/scholar?q=Reconfigurable+Framework+for+Resilient+Semantic+Segmentation+for+Space+Applications
4. Systematic Reliability Evaluation of FPGA Implemented CNN Accelerators — Zhen Gao, Shihui Gao, Yi Yao, Qiang Liu, Shulin Zeng, Guangjun Ge, Yu Wang, Anees Ullah, Pedro Reviriego, 2023
https://scholar.google.com/scholar?q=Systematic+Reliability+Evaluation+of+FPGA+Implemented+CNN+Accelerators
5. Online continual streaming learning for embedded space applications — Van-Tam Nguyen, Alaa Mazouz, 2024
https://scholar.google.com/scholar?q=Online+continual+streaming+learning+for+embedded+space+applications
6. A Quarter of a Century of Neuromorphic Architectures on FPGAs -- an Overview — Wiktor J. Szczerek, Artur Podobas, 2025
https://scholar.google.com/scholar?q=A+Quarter+of+a+Century+of+Neuromorphic+Architectures+on+FPGAs+--+an+Overview
7. Hardware platforms enabling edge AI for space applications: A critical review — unknown, approximate recent review authors, 2024 or 2025
https://scholar.google.com/scholar?q=Hardware+platforms+enabling+edge+AI+for+space+applications%3A+A+critical+review
8. Study of Radiation Effects on FPGA and GPU based Neural Networks Accelerator Designs — unknown, approximate recent systems authors, 2024 or 2025
https://scholar.google.com/scholar?q=Study+of+Radiation+Effects+on+FPGA+and+GPU+based+Neural+Networks+Accelerator+Designs
9. A Radiation-Hardened Neuromorphic Imager with Self-Healing Spiking Pixels and Unified Spiking Neural Network for Space Robotics — unknown, approximate neuromorphic hardware authors, 2024 or 2025
https://scholar.google.com/scholar?q=A+Radiation-Hardened+Neuromorphic+Imager+with+Self-Healing+Spiking+Pixels+and+Unified+Spiking+Neural+Network+for+Space+Robotics
10. Onboard Optimization and Learning: A Survey — unknown, approximate recent survey authors, 2024 or 2025
https://scholar.google.com/scholar?q=Onboard+Optimization+and+Learning%3A+A+Survey
11. Review on hardware devices and software techniques enabling neural network inference onboard satellites — unknown, approximate recent review authors, 2024 or 2025
https://scholar.google.com/scholar?q=Review+on+hardware+devices+and+software+techniques+enabling+neural+network+inference+onboard+satellites
12. AI Post Transformers: FlatAttention for Tile-Based Accelerator Inference — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-flatattention-for-tile-based-accelerator-56e6ca.mp3
13. AI Post Transformers: AWQ: On-Device LLM Compression and Acceleration — Hal Turing & Dr. Ada Shannon, 2025
https://podcast.do-not-panic.com/episodes/awq-on-device-llm-compression-and-acceleration/
14. AI Post Transformers: KVSwap for Disk-Aware Long-Context On-Device Inference — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-16-kvswap-for-disk-aware-long-context-on-de-f3c15e.mp3
Interactive Visualization: FPGA Neural Network Accelerators for Space