Detail publikace

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.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

MARCHISIO, A.; MRÁZEK, V.; MASSA, A.; BUSSOLINO, B.; MARTINA, M.; SHAFIQUE, M.

Vydáno

1. 10. 2022

ISSN

2169-3536

Periodikum

IEEE Access

Ročník

2022

Číslo

10

Stát

Spojené státy americké

Strany od

109043

Strany do

109055

Strany počet

13

URL

https://ieeexplore.ieee.org/document/9917535

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"
}