activity
20172021
most citedE-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks

36 citations · 38 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AR2021★ 1 cited

A Variable Vector Length SIMD Architecture for HW/SW Co-designed Processors

Rakesh Kumar, Alejandro Martinez, Antonio Gonzalez

Hardware/Software (HW/SW) co-designed processors provide a promising solution to the power and complexity problems of the modern microprocessors by keeping their hardware simple. M…

cs.AR2020★ 1 cited

Irregular Accesses Reorder Unit: Improving GPGPU Memory Coalescing for Graph-Based Workloads

Albert Segura, Jose-Maria Arnau, Antonio Gonzalez

GPGPU architectures have become established as the dominant parallelization and performance platform achieving exceptional popularization and empowering domains such as regular alg…

eess.SP2019

Boosting LSTM Performance Through Dynamic Precision Selection

Franyell Silfa, Jose-Maria Arnau, Antonio Gonzàlez

The use of low numerical precision is a fundamental optimization included in modern accelerators for Deep Neural Networks (DNNs). The number of bits of the numerical representation…

cs.LG2019

(Pen-) Ultimate DNN Pruning

Marc Riera, Jose-Maria Arnau, Antonio Gonzalez

DNN pruning reduces memory footprint and computational work of DNN-based solutions to improve performance and energy-efficiency. An effective pruning scheme should be able to syste…

cs.AR2018

Rendering Elimination: Early Discard of Redundant Tiles in the Graphics Pipeline

Martí Anglada, Enrique de Lucas, Joan-Manuel Parcerisa +3

GPUs are one of the most energy-consuming components for real-time rendering applications, since a large number of fragment shading computations and memory accesses are involved. M…

cs.AR2018

Trends in Processor Architecture

Antonio Gonzalez

This paper presents an overview of the main trends in processor architecture. It starts with an analysis of the past evolution of processors and the main driving forces behind it,…

cs.NE2017★ 36 cited

E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks

Franyell Silfa, Gem Dot, Jose-Maria Arnau +1

Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memo…