arXiv subjects

Charles P. Rizzo

Publications and source records attributed to Charles P. Rizzo.

3 recordsLinked to original sources

arXiv2026

A Spatiotemporal Extension of the Neuromorphic DBSCAN Implementation

DBSCAN is an algorithm that denoises and clusters data. In prior work, we implemented the DBSCAN algorithm neuromorphically, introducing two constructions termed ``flat'' and ``systolic''. The ``flat'' construction prioritizes throughput, while the ``systolic'' construction trades time for space resulting in a smaller, more hardware-friendly architecture at the cost of throughput. In this work, we offer spatiotemporal extensions of these two constructions to better leverage the spatiotemporal nature of event sensor data. Moreover, as in our prior work, we discuss partial or segmented implementations that further leverage time for space when hardware resources are constrained. All network constructions are provided as open-source implementations.

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arXiv2024

A Neuromorphic Implementation of the DBSCAN Algorithm

DBSCAN is an algorithm that performs clustering in the presence of noise. In this paper, we provide two constructions that allow DBSCAN to be implemented neuromorphically, using spiking neural networks. The first construction is termed "flat," resulting in large spiking neural networks that compute the algorithm quickly, in five timesteps. Moreover, the networks allow pipelining, so that a new DBSCAN calculation may be performed every timestep. The second construction is termed "systolic", and generates much smaller networks, but requires the inputs to be spiked in over several timesteps, column by column. We provide precise specifications of the constructions and analyze them in practical neuromorphic computing settings. We also provide an open-source implementation.

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arXiv2024

Speed-based Filtration and DBSCAN of Event-based Camera Data with Neuromorphic Computing

Spiking neural networks are powerful computational elements that pair well with event-based cameras (EBCs). In this work, we present two spiking neural network architectures that process events from EBCs: one that isolates and filters out events based on their speeds, and another that clusters events based on the DBSCAN algorithm.

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