285 citations · 838 across the 56 of their papers we have counts for
18 papers · 1 filter
LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
Berkay Döner, Thorir Mar Ingolfsson, Luca Benini +1
Electroencephalography (EEG) offers a non-invasive lens into human brain activity, but building large-scale models is hampered by topological heterogeneity: each public EEG data de…
Optimal Brain Restoration for Joint Quantization and Sparsification of LLMs
Hang Guo, Yawei Li, Luca Benini
Recent advances in Large Language Model (LLM) compression, such as quantization and pruning, have achieved notable success. However, as these techniques gradually approach their re…
A Compute&Memory Efficient Model-Driven Neural 5G Receiver for Edge AI-assisted RAN
Mahdi Abdollahpour, Marco Bertuletti, Yichao Zhang +3
Artificial intelligence approaches for base-band processing for radio receivers have demonstrated significant performance gains. Most of the proposed methods are characterized by h…
WaveFormer: A Lightweight Transformer Model for sEMG-based Gesture Recognition
Yanlong Chen, Mattia Orlandi, Pierangelo Maria Rapa +3
Human-machine interaction, particularly in prosthetic and robotic control, has seen progress with gesture recognition via surface electromyographic (sEMG) signals.However, classify…
PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation
Yanlong Chen, Mattia Orlandi, Pierangelo Maria Rapa +3
Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for analysis. Additionally, these s…
FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Efficient Multi-Head Attention on Tile-Based Many-PE Accelerators
Chi Zhang, Luca Colagrande, Renzo Andri +6
Multi-Head Attention (MHA) is a critical computational kernel in transformer-based AI models. Emerging scalable tile-based accelerator architectures integrate increasing numbers of…