71 citations · 90 across the 8 of their papers we have counts for
8 papers
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…
Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning
Andrea Bussolan, Oliver Avram, Andrea Pignata +3
With the advent of Industry 5.0, manufacturers are increasingly prioritizing worker well-being alongside mass customization. Stress-aware Human-Robot Collaboration (HRC) plays a cr…
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)…
ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
Zihao Chen, Zhili Xiao, Mahmoud Akl +17
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mec…
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…