no code implementations • 11 Sep 2023 • Federico Rossi, Marco Cococcioni, Roger Ferrer Ibàñez, Jesùs Labarta, Filippo Mantovani, Marc Casas, Emanuele Ruffaldi, Sergio Saponara
As recently demonstrated, Deep Neural Networks (DNN), usually trained using single precision IEEE 754 floating point numbers (binary32), can also work using lower precision.
no code implementations • 18 Sep 2020 • Marc Ortiz, Florian Scheidegger, Marc Casas, Cristiano Malossi, Eduard Ayguadé
In this work, we leverage ensemble learning as a tool for the creation of faster, smaller, and more accurate deep learning models.
1 code implementation • 5 Apr 2020 • Sicong Zhuang, Cristiano Malossi, Marc Casas
This paper reduces the cost of DNNs training by decreasing the amount of data movement across heterogeneous architectures composed of several GPUs and multicore CPU devices.
Distributed, Parallel, and Cluster Computing
no code implementations • 25 Sep 2019 • John Osorio, Adrià Armejach, Eric Petit, Marc Casas
The first approach achieves accuracy ratios slightly slower than the state-of-the-art by using half-precision arithmetic during more than 99% of training.
no code implementations • 14 Apr 2018 • Marc Ortiz, Adrián Cristal, Eduard Ayguadé, Marc Casas
The use of low-precision fixed-point arithmetic along with stochastic rounding has been proposed as a promising alternative to the commonly used 32-bit floating point arithmetic to enhance training neural networks training in terms of performance and energy efficiency.