FPGA-Accelerated Decision Tree Classifier for Real-Time Supervision of Bluetooth SoC
Conference
Elkanishy, A, Rivera, DT, Furth, PM et al. (2019). FPGA-Accelerated Decision Tree Classifier for Real-Time Supervision of Bluetooth SoC
. 10.1109/ReConFig48160.2019.8994784
Elkanishy, A, Rivera, DT, Furth, PM et al. (2019). FPGA-Accelerated Decision Tree Classifier for Real-Time Supervision of Bluetooth SoC
. 10.1109/ReConFig48160.2019.8994784
Wireless communication protocols are used in all smart devices and systems. This work proposes an FPGA-accelerated supervisory system that classifies the operation of a communication system-on-chip (SoC). In this work, the selected communication protocol is Bluetooth (BT). The input supply current to the transceiver block of the SoC is monitored and sampled at 50 kHz. We extract simple descriptive features from the transceiver input power signal and use them to train a machine learning (ML) model to classify two different BT operation modes. We implemented ADC sampling, feature extraction, and a real-time decision tree classifier on an Intel MAX 10 FPGA. The measured classification accuracy is 97.4%.
