1 code implementation • 11 Oct 2022 • Alberto Marchisio, Vojtech Mrazek, Andrea Massa, Beatrice Bussolino, Maurizio Martina, Muhammad Shafique
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints.
no code implementations • 26 Sep 2022 • Renzo Andri, Beatrice Bussolino, Antonio Cipolletta, Lukas Cavigelli, Zhe Wang
The Winograd-enhanced DSA achieves up to 1. 85x gain in energy efficiency and up to 1. 83x end-to-end speed-up for state-of-the-art segmentation and detection networks.
no code implementations • 21 Jun 2022 • Alberto Marchisio, Beatrice Bussolino, Edoardo Salvati, Maurizio Martina, Guido Masera, Muhammad Shafique
In our experiments, we evaluate tradeoffs between area, power consumption, and critical path delay of the designs implemented with the ASIC design flow, and the accuracy of the quantized CapsNets, compared to the exact functions.
no code implementations • 21 Dec 2020 • Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera, Maurizio Martina, Muhammad Shafique
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life.
1 code implementation • 19 Aug 2020 • Alberto Marchisio, Andrea Massa, Vojtech Mrazek, Beatrice Bussolino, Maurizio Martina, Muhammad Shafique
Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications.
no code implementations • 15 Apr 2020 • Alberto Marchisio, Beatrice Bussolino, Alessio Colucci, Maurizio Martina, Guido Masera, Muhammad Shafique
Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the traditional CNNs.
1 code implementation • 24 May 2019 • Alberto Marchisio, Beatrice Bussolino, Alessio Colucci, Muhammad Abdullah Hanif, Maurizio Martina, Guido Masera, Muhammad Shafique
The goal is to reduce the hardware requirements of CapsNets by removing unused/redundant connections and capsules, while keeping high accuracy through tests of different learning rate policies and batch sizes.