kNN-STUFF: kNN STreaming Unit for Fpgas

Abstract

This paper presents kNN STreaming Unit For Fpgas (kNN-STUFF), a modular, scalable and efficient Hardware/Software implementation of k-Nearest Neighbors (kNN) classifier targeting System on Chip (SoC) devices. It takes advantage of custom accelerators, implemented on the reconfigurable fabric of the SoC device, to perform most of the classifier's workload, whereas the processor coordinates the accelerators and runs the remaining workload of the kNN algorithm. kNN-STUFF offers a highly flexible framework, where the designer has the possibility to define the number of parallel instances of the classifier and the parallelism within each instance. This capability allows creating the most suitable implementation for a target device of any size. Results show that kNN-STUFF, with 24 accelerators, attains performance improvements up to 67.4×, when compared to an optimized (-O3) software-only implementation of the kNN running on a single core of the ARM Cortex-A9 CPU. Furthermore, its energy efficiency improvements are as high as 50.6×.

Keywords

• FPGA
• SoC
• kNN
• Parallel architectures
• real-time classification
• IoT