6 papers
Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks
Aidin Attar, Eleonora Cicciarella, Michele Rossi
We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-first-spike (TTFS) latencies. Our key research question is which archi…
Combining Convolution and Delay Learning in Recurrent Spiking Neural Networks
Lúcio Folly Sanches Zebendo, Eleonora Cicciarella, Michele Rossi
Spiking neural networks (SNNs) are rapidly gaining momentum as an alternative to conventional artificial neural networks in resource constrained edge systems. In this work, we cont…
Sparse Spike Encoding of Channel Responses for Energy Efficient Human Activity Recognition
Eleonora Cicciarella, Riccardo Mazzieri, Jacopo Pegoraro +1
ISAC enables pervasive monitoring, but modern sensing algorithms are often too complex for energy-constrained edge devices. This motivates the development of learning techniques th…
Convolutional Spiking-based GRU Cell for Spatio-temporal Data
Yesmine Abdennadher, Eleonora Cicciarella, Michele Rossi
Spike-based temporal messaging enables SNNs to efficiently process both purely temporal and spatio-temporal time-series or event-driven data. Combining SNNs with Gated Recurrent Un…
Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Riccardo Mazzieri, Eleonora Cicciarella, Jacopo Pegoraro +2
Radar-based Human Activity Recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the firs…
Learned Spike Encoding of the Channel Response for Low-Power Environment Sensing
Eleonora Cicciarella, Riccardo Mazzieri, Jacopo Pegoraro +1
Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the e…