Real-time FPGA-based Anomaly Detection for Radio Frequency Signals
Real-time FPGA-based Anomaly Detection for Radio Frequency Signals
复制标题
基于 FPGA 的射频信号实时异常检测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
P. Leong
中科院分区：
文献类型：
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作者：
Duncan J. M. Moss;D. Boland;P. Pourbeik;P. Leong
我们描述了一个开源的，FPGA加速的基于神经网络的异常检测器。检测器从观察到的样本数据中导出其训练集，并且可以以无监督的方式进行软件中的连续学习。经过训练的网络权重被传递到FPGA，FPGA执行连续的高速异常检测，结合并行性降低的精度和单芯片设计，以最大限度地提高性能和能源效率。我们的设计可以处理连续200 MS/s的复杂输入，以相同的速率产生异常分类，延迟为105 ns，比软件无线电（如GNU Radio）至少提高了4个数量级。
We describe an open source, FPGA accelerated neural network-based anomaly detector. The detector derives its training set from observed exemplar data and continuous learning in software can be undertaken in an unsupervised manner. Trained network weights are passed to the FPGA, which performs continuous high-speed anomaly detection, combining parallelism reduced precision, and a single-chip design to maximise performance and energy efficiency. Our design can process continuous 200 MS/s complex inputs, producing anomaly classifications at the same rate, with a latency of 105 ns, an improvement of at least 4 orders of magnitude over a software radio such as GNU Radio.