50 citations · 120 across the 20 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…