5 papers
Efficient Semantic Understanding from Digital Foveation
Caterina Caccavella, Vittorio Fra, Andreas Ziegler +2
Dense semantic segmentation allocates computational resources uniformly across the entire image, regardless of scene complexity or task relevance. Inspired by biological vision, we…
Heterogeneous SoC Integrating an Open-Source Recurrent SNN Accelerator for Neuromorphic Edge Computing on FPGA
Michelangelo Barocci, Vittorio Fra, Enrico Macii +1
The growing popularity of Spiking Neural Networks (SNNs) and their applications has led to a significant fast-paced increase of neuromorphic architectures capable of mimicking the…
Modeling and Optimizing Performance Bottlenecks for Neuromorphic Accelerators
Jason Yik, Walter Gallego Gomez, Andrew Cheng +8
Neuromorphic accelerators offer promising platforms for machine learning (ML) inference by leveraging event-driven, spatially-expanded architectures that naturally exploit unstruct…
Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
Vittorio Fra
Hyperparameter optimization (HPO) is of paramount importance in the development of high-performance, specialized artificial intelligence (AI) models, ranging from well-established…
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices
Vittorio Fra, Benedetto Leto, Andrea Pignata +2
Neuromorphic models take inspiration from the human brain by adopting bio-plausible neuron models to build alternatives to traditional Machine Learning (ML) and Deep Learning (DL)…