2 papers
cs.AR2025
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…
cs.NE2024
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)…