Pedro Tomás

2 accepted papers

2021

Positnn: Training Deep Neural Networks with Mixed Low-Precision Posit

ICASSP 2021accepted

Low-precision formats have proven to be an efficient way to reduce not only the memory footprint but also the hardware resources and power consumption of deep learning computations. Under this premise, the posit numerical format appears to be a highly viable substitute for the IEEE floating-point, b…

Cited by 0 Source ↗Scholar ↗

2020

Processing Convolutional Neural Networks on Cache

João Vieira, Nuno Roma, Gabriel Falcão, Pedro Tomás

ICASSP 2020accepted

With the advent of Big Data application domains, several Machine Learning (ML) signal-processing algorithms, such as Convolutional Neural Networks (CNNs), are required to process progressively larger datasets at a great cost in terms of both compute power and memory bandwidth. Although dedicated acc…

Cited by 0 Source ↗Scholar ↗