457 citations · 648 across the 21 of their papers we have counts for
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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…