129 citations · 462 across the 26 of their papers we have counts for
5 papers · 1 filter
ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers
Michal Pinos, Lukas Sekanina, Vojtech Mrazek
Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and hig…
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks
Alberto Marchisio, Vojtech Mrazek, Andrea Massa +3
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are comput…
DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware
Alberto Marchisio, Vojtech Mrazek, Muhammad Abdullah Hanif +1
Deep Neural Networks (DNNs) have been established as the state-of-the-art algorithm for advanced machine learning applications. Recently proposed by the Google Brain's team, the Ca…
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks
Alberto Marchisio, Andrea Massa, Vojtech Mrazek +3
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. Recently the Go…
ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations
Alberto Marchisio, Vojtech Mrazek, Muhammad Abudllah Hanif +1
Recent advances in Capsule Networks (CapsNets) have shown their superior learning capability, compared to the traditional Convolutional Neural Networks (CNNs). However, the extreme…