71 citations · 76 across the 8 of their papers we have counts for
8 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)…
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Jens E. Pedersen, Steven Abreu, Matthias Jobst +12
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine…
WaLiN-GUI: a graphical and auditory tool for neuron-based encoding
Simon F. Müller-Cleve, Fernando M. Quintana, Vittorio Fra +4
Neuromorphic computing relies on spike-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spik…
Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware
Simon F Muller-Cleve, Vittorio Fra, Lyes Khacef +8
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstan…