12 citations · 26 across the 7 of their papers we have counts for
8 papers
Beyond Reproducibility: Token Probabilities Expose Large Language Model Nondeterminism
Tairan Fu, Gonzalo Martínez, Javier Conde +4
The execution of Large Language Models (LLMs) has been shown to produce nondeterministic results when run on Graphics Processing Units (GPUs), even when they are configured to prod…
Energy-Efficient Stochastic Computing (SC) Neural Networks for Internet of Things Devices With Layer-Wise Adjustable Sequence Length (ASL)
Ziheng Wang, Pedro Reviriego, Farzad Niknia +4
Stochastic computing (SC) has emerged as an efficient low-power alternative for deploying neural networks (NNs) in resource-limited scenarios, such as the Internet of Things (IoT).…
Speed and Conversational Large Language Models: Not All Is About Tokens per Second
Javier Conde, Miguel González, Pedro Reviriego +3
The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the mo…
Can ChatGPT Learn to Count Letters?
Javier Conde, Gonzalo Martínez, Pedro Reviriego +3
Large language models (LLMs) struggle on simple tasks such as counting the number of occurrences of a letter in a word. In this paper, we investigate if ChatGPT can learn to count…
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things
Ziheng Wang, Pedro Reviriego, Farzad Niknia +3
The implementation of machine learning in Internet of Things devices poses significant operational challenges due to limited energy and computation resources. In recent years, sign…
Dependability Evaluation of Stable Diffusion with Soft Errors on the Model Parameters
Zhen Gao, Lini Yuan, Pedro Reviriego +2
Stable Diffusion is a popular Transformer-based model for image generation from text; it applies an image information creator to the input text and the visual knowledge is added in…
Concurrent Linguistic Error Detection (CLED): a New Methodology for Error Detection in Large Language Models
Jinhua Zhu, Javier Conde, Zhen Gao +3
The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thu…
Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems
Pedro Reviriego, Ziheng Wang, Alvaro Alonso +4
The complexity of Machine Learning (ML) systems increases each year, with current implementations of large language models or text-to-image generators having billions of parameters…