FPGA-Accelerated Time Series Mining on Low-Power IoT Devices
Seongyoung Kang1, Jinyeong Moon2 and Sang-Woo Jun3
1 Kookmin University, Seoul, South Korea 2 Florida State University, Tallahassee, USA 3 University of California, Irvine, USA
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.
[The authors opted for not publicly sharing a presentation video.]