Ultra-low Power Current Mode Distance Calculation Circuit for K-Means Clustering

K-means clustering algorithm partitions data into k unique and non-overlapping sets. Although the method primarily uses Euclidean distance, dissimilarity within datasets can be assessed using a variety of metrics. This paper focuses on assessing the performance of the K-means clustering algorithm using three distinct distance metrics: (i) Euclidean distance(L2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> norm), (ii) A proposed circuit design distance metric, and (iii) Manhattan distance(L2norm). However, integrating this type of functionality within a traditional CMOS design demands expensive hardware and results in considerable power consumption. The design’s performance was assessed using a standard K-means clustering algorithm with K=4 on the "Iris flower dataset". Findings indicate a classification throughput of 500 ns and a power consumption below 1 μW, achieving significantly lower energy consumption compared to a conventional digital CMOS design in 65 nm commercial CMOS technology.