Publication Details

RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

MARCHISIO, A.; MRÁZEK, V.; MASSA, A.; BUSSOLINO, B.; MARTINA, M.; SHAFIQUE, M. RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks. IEEE Access, 2022, vol. 2022, no. 10, p. 109043-109055. ISSN: 2169-3536.
Czech title
RoHNAS: Systém pro automatický návrh architektur neuronových sítí s optimalizací pro odolnost proti útokům a hardwarovou efektivitou pro konvoluční a kapsulové sítě
Type
journal article
Language
English
Authors
MARCHISIO, A.
Mrázek Vojtěch, Ing., Ph.D. (DCSY)
MASSA, A.
BUSSOLINO, B.
MARTINA, M.
Shafique Muhammad
URL
Keywords

Adversarial Robustness, Energy Efficiency, Latency, Memory, Hardware-Aware Neural
Architecture Search, Evolutionary Algorithm, Deep Neural Networks, Capsule
Networks

Abstract

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural
Network (DNN) architectures for a given application under given system
constraints. DNNs are computationally-complex as well as vulnerable to
adversarial attacks. In order to address multiple design objectives, we propose
RoHNAS, a novel NAS framework that jointly optimizes for adversarial-robustness
and hardware-efficiency of DNNs executed on specialized hardware accelerators.
Besides the traditional convolutional DNNs, RoHNAS additionally accounts for
complex types of DNNs such as Capsule Networks. For reducing the exploration
time, RoHNAS analyzes and selects appropriate values of adversarial perturbation
for each dataset to employ in the NAS flow. Extensive evaluations on multi -
Graphics Processing Unit (GPU) - High Performance Computing (HPC) nodes provide
a set of Pareto-optimal solutions, leveraging the tradeoff between the
above-discussed design objectives. For example, a Pareto-optimal DNN for the
CIFAR-10 dataset exhibits 86.07 % accuracy, while having an energy of 38.63 mJ,
a memory footprint of 11.85 MiB, and a latency of 4.47 ms.

Published
2022
Pages
109043–109055
Journal
IEEE Access, vol. 2022, no. 10, ISSN 2169-3536
DOI
10.1109/ACCESS.2022.3214312
UT WoS
000870222300001
EID Scopus
2-s2.0-85140760282
BibTeX 
@article{BUT179460,
  author="MARCHISIO, A. and MRÁZEK, V. and MASSA, A. and BUSSOLINO, B. and MARTINA, M. and SHAFIQUE, M.",
  title="RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks",
  journal="IEEE Access",
  year="2022",
  volume="2022",
  number="10",
  pages="109043--109055",
  doi="10.1109/ACCESS.2022.3214312",
  issn="2169-3536",
  url="https://ieeexplore.ieee.org/document/9917535"
}
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