24 citations · 27 across the 5 of their papers we have counts for
6 papers
A Spatiotemporal Extension of the Neuromorphic DBSCAN Implementation
Charles P. Rizzo, James S. Plank
DBSCAN is an algorithm that denoises and clusters data. In prior work, we implemented the DBSCAN algorithm neuromorphically, introducing two constructions termed ``flat'' and ``sys…
Vibe2Spike: Batteryless Wireless Tags for Vibration Sensing with Event Cameras and Spiking Networks
Danny Scott, William LaForest, Hritom Das +5
The deployment of dense, low-cost sensors is critical for realizing ubiquitous smart environments. However, existing sensing solutions struggle with the energy, scalability, and re…
A Neuromorphic Implementation of the DBSCAN Algorithm
Charles P. Rizzo, James S. Plank
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 sp…
Speed-based Filtration and DBSCAN of Event-based Camera Data with Neuromorphic Computing
Charles P. Rizzo, Catherine D. Schuman, James S. Plank
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 p…
Optimizations for a Current-Controlled Memristor-based Neuromorphic Synapse Design
Hritom Das, Rocco D. Febbo, Charlie P. Rizzo +3
The synapse is a key element of neuromorphic computing in terms of efficiency and accuracy. In this paper, an optimized current-controlled memristive synapse circuit is proposed. O…
The Case for RISP: A Reduced Instruction Spiking Processor
James S. Plank, ChaoHui Zheng, Bryson Gullett +4
In this paper, we introduce RISP, a reduced instruction spiking processor. While most spiking neuroprocessors are based on the brain, or notions from the brain, we present the case…