Real-time FPGA-based Anomaly Detection for Radio Frequency Signals

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.