A parallel hardware implementation for 2-D hierarchical clustering based on fuzzy logic

In this paper we propose a novel hardware implementation for a bidimensional unconstrained hierarchical clustering method, based on fuzzy logic and membership functions. Unlike classical clustering approaches, our work is based on an advanced algorithm that shows an intrinsic parallelism. Such parallelism can be exploited to design an efficient hardware implementation suitable for low-resources, low-power and highcomputational demanding applications like smart-sensors and IoT devices. We validated our design by an extensive simulation campaign on well known 2D clustering datasets. Our solution shows the same clustering performances of the original algorithm despite the applied mathematical approximations and the small word-lengths used in the fixed point arithmetic.