50 citations · 143 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks
Alberto Dequino, Alessio Carpegna, Davide Nadalini +4
Rehearsal-based Continual Learning (CL) has been intensely investigated in Deep Neural Networks (DNNs). However, its application in Spiking Neural Networks (SNNs) has not been expl…
SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA
Dario Padovano, Alessio Carpegna, Alessandro Savino +1
One of today's main concerns is to bring Artificial Intelligence power to embedded systems for edge applications. The hardware resources and power consumption required by state-of-…
SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks
Anil Bayram Gogebakan, Enrico Magliano, Alessio Carpegna +3
As artificial neural networks become increasingly integrated into safety-critical systems such as autonomous vehicles, devices for medical diagnosis, and industrial automation, ens…
Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge
Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
Including Artificial Neural Networks in embedded systems at the edge allows applications to exploit Artificial Intelligence capabilities directly within devices operating at the ne…
Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks
Sepide Saeedi, Alessio Carpegna, Alessandro Savino +1
Approximate Computing (AxC) techniques trade off the computation accuracy for performance, energy, and area reduction gains. The trade-off is particularly convenient when the appli…
Spiker: an FPGA-optimized Hardware acceleration for Spiking Neural Networks
Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient Artificial Neural Network (ANN). This work presents the development of a hardware acceler…