A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

no code implementations • 29 Nov 2023 • Fabrizio Ferrandi, Serena Curzel, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Alessio Burrello, Francesco Barchi, Luca Benini, Luciano Lavagno, Teodoro Urso, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Nicola Petra, Davide De Caro, Gennaro Di Meo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Francesco Silvestri, Paolo Palazzari, Stefania Perri

This survey provides a holistic review of the most influential design methodologies and EDA tools proposed in recent years to implement Deep Learning accelerators, offering the reader a wide perspective in this rapidly evolving field.

A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms

no code implementations • 27 Jun 2023 • Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi, Leandro Fiorin, Serena Curzel, Luca Benini, Francesco Conti, Angelo Garofalo, Cristian Zambelli, Enrico Calore, Sebastiano Fabio Schifano, Maurizio Palesi, Giuseppe Ascia, Davide Patti, Stefania Perri, Nicola Petra, Davide De Caro, Luciano Lavagno, Teodoro Urso, Valeria Cardellini, Gian Carlo Cardarilli, Robert Birke

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable solution for several classes of high-performance computing (HPC) applications such as image classification, computer vision, and speech recognition.

Image Classification speech-recognition +1

A Survey on Compiler Autotuning using Machine Learning

no code implementations • 13 Jan 2018 • Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, Cristina Silvano

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems.

BIG-bench Machine Learning Compiler Optimization