FPGA-Accelerated Time Series Mining on Low-Power IoT Devices

Abstract

We present a case for FPGA-accelerated edge processing for low-power Internet-of-Things (IoT) devices, using time series similarity search as a driving application. As the data collection capabilities of low-power IoT device increase, the primary constraint on their capacity is becoming the resource requirements of wirelessly transferring collected data to a central repository. This work presents a solution to this limitation by augmenting the IoT device with a inexpensive, power-efficient FPGA accelerator, which can perform fairly complex edge mining operations and drastically reduce the wireless data transfer requirements. This approach reduces the total power consumption of the device despite the added FPGA component, while also reducing the computation requirements at the central server. We use the Dynamic Time Warping (DTW) algorithm as an example workload. Using a low-cost Lattice iCE40 UltraPlus FPGA, we demonstrate that the FPGA-augmented mining algorithm can both support significantly higher data collection rate while improving the computation power efficiency of the entire deployment by an order of magnitude.