Orateur
Description
Deep Learning has been the most significant breakthrough in the past 10 years in the field of pattern recognition and machine learning. It has achieved significant advancements in terms of the effectiveness of prediction models on many research topics and application fields, ranging from computer vision, natural language processing, embodied AI and to more traditional fields of pattern recognition. This paradigm shift has radically changed the research methodology towards a data-oriented approach, in which learning involves all steps of the prediction pipeline from feature extraction to classification. While research efforts have concentrated on the design of effective feature extraction and prediction architectures, computation has moved from CPU-only approaches to the dominant use of GPUs and massively parallel devices, empowered by large-scale and highly dimensional datasets.
The goal of this talk is to present recent advancements in AI and techniques for training deep neural networks on multi-GPU technology to shorten the training time required for data-intensive applications.
Giuseppe Fiameni is a Solution Architect at NVIDIA where he oversees the NVIDIA AI Technology Centre in Italy, a collaboration among NVIDIA, CINI and CINECA to accelerate academic research in the field of Artificial Intelligence through collaboration projects. He has been working as HPC specialist at CINECA, the largest HPC facility in Italy, for more than 14 years providing support for large-scale data analytics workloads.
