2 citations · 3 across the 5 of their papers we have counts for
5 papers
GeoIMO: Geometry-Driven Independent Motion Classification for Event Cameras
Anil Bayram Gogebakan, Filippo Marostica, Alessio Caviglia +2
Existing automotive event datasets rely on appearance-based annotations from frame pipelines, making them poorly suited for motion-aware event perception. We present a geometry-dri…
Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks
Alessio Caviglia, Filippo Marostica, Alessandro Savino +1
Deploying adaptive intelligence at the edge remains challenging due to the high computational and energy cost of training neural models. Spiking Neural Networks (SNNs) offer a prom…
NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
Alessio Caviglia, Filippo Marostica, Roberta Bardini +2
The rapid expansion of spiking neural networks (SNNs) has led to a proliferation of training algorithms that differ widely in biological inspiration, computational structure, and h…
SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
Alessio Caviglia, Filippo Marostica, Alessio Carpegna +2
Hardware accelerators are essential for achieving low-latency, energy-efficient inference in edge applications like image recognition. Spiking Neural Networks (SNNs) are particular…
Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
Filippo Marostica, Alessio Carpegna, Alessandro Savino +1
This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. W…