FPGA-Based Neural Network Accelerators for Space Applications: A Survey

• URL: http://arxiv.org/abs/2504.16173v2
• Date: Thu, 24 Apr 2025 12:04:11 GMT
• Title: FPGA-Based Neural Network Accelerators for Space Applications: A Survey
• Authors: Pedro Antunes, Artur Podobas,
• Abstract summary: Field-programmable gate arrays (FPGAs) have garnered significant interest due to their flexibility, cost-effectiveness, and radiation tolerance potential.<n>This survey serves as a valuable resource for researchers aiming to implement FPGA-based NN accelerators in space applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Space missions are becoming increasingly ambitious, necessitating high-performance onboard spacecraft computing systems. In response, field-programmable gate arrays (FPGAs) have garnered significant interest due to their flexibility, cost-effectiveness, and radiation tolerance potential. Concurrently, neural networks (NNs) are being recognized for their capability to execute space mission tasks such as autonomous operations, sensor data analysis, and data compression. This survey serves as a valuable resource for researchers aiming to implement FPGA-based NN accelerators in space applications. By analyzing existing literature, identifying trends and gaps, and proposing future research directions, this work highlights the potential of these accelerators to enhance onboard computing systems.

Related papers

• Optimizing Earth-Moon Transfer and Cislunar Navigation: Integrating Low-Energy Trajectories, AI Techniques and GNSS-R Technologies [0.0]
  cislunar activities, including lunar landings, require cost-efficient trajectory design and reliable integration of navigation and remote sensing.<n>Traditional Earth-Moon transfers suffer from rigid launch windows and high propellant demands, while Earth-based systems provide little to no coverage beyond geostationary orbit.<n>This review compares four major transfer strategies by evaluating velocity requirements, flight durations, and fuel efficiency, and by identifying their suitability for both crewed and robotic missions.
  arXiv  Detail & Related papers  (2025-11-05T04:41:43Z)
• Guidance and Control Neural Network Acceleration using Memristors [3.0184858893569353]
  This paper explores the use of Phase-Change Memory (PCM) and Resistive Random-Access Memory (RRAM) memristors for on-board in-memory computing AI acceleration in space applications.<n>We show that the memristive accelerator is able to learn the expert actions, though challenges remain with the impact of noise on accuracy.
  arXiv  Detail & Related papers  (2025-09-02T14:33:00Z)
• INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks [51.76364085699241]
  INSPIRE-GNN is a novel Reinforcement Learning-boosted hybrid Graph Neural Network (GNN) framework designed to optimize sensor placement and improve link-level bicycling volume estimation in data-sparse environments.<n>Our framework outperforms traditional methods for sensor placement such as betweenness centrality, closeness, observed bicycling activity and random placement, across key metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE)<n>Our experiments benchmark INSPIRE-GNN against standard machine learning and deep learning models in the bicycle volume estimation performance, underscoring its effectiveness
  arXiv  Detail & Related papers  (2025-07-31T20:00:35Z)
• SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST [39.79758414095764]
  Spiking Neural Networks (SNNs) are promising due to their event-driven and temporally sparse nature.<n>This paper explores using the open-source Spiker+ framework to generate optimized SNNs accelerators for handwritten digit recognition.
  arXiv  Detail & Related papers  (2025-07-04T08:22:13Z)
• Energy efficiency analysis of Spiking Neural Networks for space applications [43.91307921405309]
  Spiking Neural Networks (SNN) are highly attractive due to their theoretically superior energy efficiency.<n>This work presents a numerical analysis and comparison of different SNN techniques applied to scene classification for the EuroSAT dataset.
  arXiv  Detail & Related papers  (2025-05-16T16:29:50Z)
• Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA [20.629635991749808]
  This paper proposes an algorithm and hardware co-design framework that can generate field-programmable gate array (FPGA)-based accelerators for efficient BayesNNs. At the algorithm level, we propose novel multi-exit dropout-based BayesNNs with reduced computational and memory overheads. At the hardware level, this paper introduces a transformation framework that can generate FPGA-based accelerators for the proposed efficient BayesNNs.
  arXiv  Detail & Related papers  (2024-06-20T17:08:42Z)
• Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC [0.13108652488669734]
  This article presents a pioneering approach to real-time spacecraft pose estimation, utilizing a mixed-precision quantized neural network implemented on the FPGA components of a commercially available MPSoC. Our contribution includes the first real-time, open-source implementation of such algorithms, marking a significant advancement in making efficient spacecraft pose estimation algorithms widely accessible.
  arXiv  Detail & Related papers  (2024-06-06T17:36:26Z)
• SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA [42.170149806080204]
  SpikExplorer is a Python tool for hardware-oriented Automatic Design Space Exploration. It searches the optimal network architecture, neuron model, and internal and training parameters. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180mW/image and a latency of 0.12 ms/image.
  arXiv  Detail & Related papers  (2024-04-04T17:53:08Z)
• Enabling Accelerators for Graph Computing [0.0]
  Graph Neural Networks (GNNs) offer a novel paradigm for learning on graph-structured data. GNNs present new computational challenges compared to conventional neural networks. This thesis aims to develop a better understanding of how GNNs interact with the underlying hardware.
  arXiv  Detail & Related papers  (2023-12-16T23:31:20Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware [30.525912505620685]
  Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability challenges when it comes to real-world applications. We provide a taxonomy of GNN acceleration, review the existing approaches, and suggest future research directions.
  arXiv  Detail & Related papers  (2023-06-24T20:20:45Z)
• Resource-constrained FPGA Design for Satellite Component Feature Extraction [0.0]
  This work proposes use of neural network-based object detection algorithm that can be deployed on a resource-constrained FPGA. Hardware-in-the-loop experiments were performed on the ORION Maneuver Kinematics Simulator at Florida Tech. Results show the FPGA implementation increases the throughput and decreases latency while maintaining comparable accuracy.
  arXiv  Detail & Related papers  (2023-01-22T04:49:04Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU [58.720142291102135]
  In this paper we propose a pose estimation software exploiting neural network architectures. We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
  arXiv  Detail & Related papers  (2022-04-07T08:53:18Z)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
  A fully-autonomous aerial robot for high-speed object grasping has been proposed. As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface. Our approach has been validated in a challenging international competition and has shown outstanding results.
  arXiv  Detail & Related papers  (2021-12-10T11:49:51Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.