FPGA-Accelerated Decision Tree Classifier for Real-Time Supervision of Bluetooth SoC (original) (raw)

2019 International Conference on ReConFigurable Computing and FPGAs (ReConFig), 2019

Abstract

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%.

Abdel-Hameed A. Badawy hasn't uploaded this paper.

Let Abdel-Hameed A. know you want this paper to be uploaded.

Ask for this paper to be uploaded.