综述基于FPGA的神经网络加速器在航天中的应用前景
FPGA-Based Neural Network Accelerators for Space Applications: A Survey
- 分析已有文献，梳理FPGA在航天中部署NN加速器的方法
- 指出辐射耐受与能效是关键挑战，现有方案存在优化空间
- 适合航天计算、嵌入式AI研究者参考
航天任务日益复杂，对星载高性能计算系统提出更高要求。为此，现场可编程门阵列（FPGA）因其灵活性、成本效益和潜在的抗辐射能力受到广泛关注。与此同时，神经网络（NN）在自主运行、传感器数据处理和数据压缩等任务中展现出强大潜力。本综述为研究人员在航天应用中实现基于FPGA的神经网络加速器提供重要参考。通过分析现有文献，识别趋势与空白，并提出未来研究方向，本文强调了此类加速器在提升星载计算系统性能方面的巨大潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。