285 citations · 1.1k across the 93 of their papers we have counts for
7 papers · 1 filter
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
Uzair Khan, Luigi Capogrosso, Francesco Biondani +4
Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across diffe…
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
Wei Huang, Haotong Qin, Yangdong Liu +7
Post-training quantization (PTQ) is an effective technique for compressing large language models (LLMs). However, while uniform-precision quantization is computationally efficient,…
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
Wei Huang, Xingyu Zheng, Xudong Ma +7
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the…
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
Haotong Qin, Xudong Ma, Xingyu Zheng +6
The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate yet compact LLMs for deployment on resource-constrained hardware. However, existing methods…
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Wei Huang, Yangdong Liu, Haotong Qin +5
Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a p…
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices
Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli +3
Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on Deep Neural Networks are very effective, but highly demanding in te…
FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
Xiaying Wang, Michele Magno, Lukas Cavigelli +1
The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine…