Showing papers by "Eamonn Keogh published in 2023"
1 citations
01 May 2023
TL;DR: In this article , an FPGA-based accelerator architecture that can rapidly extract features from streaming time series is presented, and the accelerator currently extracts 25 features and leaves approximately 30% of the resources unused on an AMD/Xilinx Alveo U280FPGA.
Abstract: We present an FPGA-based accelerator architecture that can rapidly extract features from streaming time series. The accelerator currently extracts 25 features, and leaves approximately 30% of the resources unused on an AMD/Xilinx Alveo U280 FPGA. The FPGA-based accelerator can extract the same set of features significantly faster than a GPU or multi-core CPU while consuming far less power. Additional features can be extracted if desired, as long as doing so does not exceed the resource capacity of the FPGA.