1 citations · 3 across the 24 of their papers we have counts for
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S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring
Glenn Anta Bucagu, Thorir Mar Ingolfsson, Yawei Li +1
Foundation models offer a promising paradigm for Electroencephalography (EEG) analysis, leveraging generalizable representations from vast unlabeled datasets. Yet, Transformer-base…
Quantizing Recursive Reasoning Models
Thorir Mar Ingolfsson, Wajeeha Tahir, Anna Tegon +3
Recursive reasoning models solve hard puzzles by applying compact, weight-tied blocks over many refinement steps. Because these blocks are reused many times, quantizing them create…
RDKV: Rate-Distortion Bit Allocation for Joint Eviction and Quantization of the KV Cache
Junkai Zhang, Hang Guo, Luca Benini +1
Large language models (LLMs) have shown strong performance across diverse tasks, but their inference with long input contexts is bottlenecked by memory size and bandwidth. The Key-…
PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence
Marija Zelic, Anna Tegon, Yawei Li +2
Physiological foundation models (FMs) have shown promise for biosignal representation learning, yet most remain confined to a single modality such as EEG, ECG, or PPG, largely beca…
FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-low Power Microcontroller
Anna Tegon, Nicholas Lehmann, Yawei Li +3
Objective: To enable continuous, long-term neuro-monitoring on wearable devices by overcoming the computational bottlenecks of Transformer-based Electroencephalography (EEG) founda…
LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling
Danaé Broustail, Anna Tegon, Thorir Mar Ingolfsson +2
Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains chal…
Revisiting Adaptive Rounding with Vectorized Reparameterization for LLM Quantization
Yuli Zhou, Qingxuan Chen, Luca Benini +2
Adaptive Rounding has emerged as an alternative to round-to-nearest (RTN) for post-training quantization by enabling cross-element error cancellation. Yet, dense and element-wise r…
TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge
Matteo Fasulo, Giusy Spacone, Thorir Mar Ingolfsson +3
Objective: Surface electromyography (EMG) is a non-invasive sensing modality widely used in biomechanics, rehabilitation, prosthetic control, and human-machine interfaces. Despite…
Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
Yanlong Chen, Amirhossein Habibian, Luca Benini +1
Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significant accuracy loss. Despite its pot…