Publication Details
ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations
Resilience, Capsule Networks, Approximation
Recent advances in Capsule Networks (CapsNets) have shown their superior learning
capability, compared to the traditional Convolutional Neural Networks (CNNs).
However, the extremely high complexity of CapsNets limits their fast deployment
in real-world applications. Moreover, while the resilience of CNNs have been
extensively investigated to enable their energy-efficient implementations, the
analysis of CapsNets resilience is a largely unexplored area, that can provide
a strong foundation to investigate techniques to overcome the CapsNets complexity
challenge.Following the trend of Approximate Computing to enable energy-efficient
designs, we perform an extensive resilience analysis of the CapsNets inference
subjected to the approximation errors. Our methodology models the errors arising
from the approximate components (like multipliers), and analyze their impact on
the classification accuracy of CapsNets. This enables the selection of
approximate components based on the resilience of each operation of the CapsNet
inference. We modify the TensorFlow framework to simulate the injection of
approximation noise (based on the models of the approximate components) at
different computational operations of the CapsNet inference. Our results show
that the CapsNets are more resilient to the errors injected in the computations
that occur during the dynamic routing (the softmax and the update of the
coefficients), rather than other stages like convolutions and activation
functions. Our analysis is extremely useful towards designing efficient CapsNet
hardware accelerators with approximate components. To the best of our knowledge,
this is the first proof-of-concept for employing approximations on the
specialized CapsNet hardware.
@inproceedings{BUT168113,
author="MARCHISIO, A. and MRÁZEK, V. and HANIF, M. and SHAFIQUE, M.",
title="ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations",
booktitle="Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020",
year="2020",
pages="1205--1210",
publisher="Institute of Electrical and Electronics Engineers",
address="Grenoble",
doi="10.23919/DATE48585.2020.9116393",
isbn="978-3-9819263-4-7",
url="https://arxiv.org/pdf/1912.00700.pdf"
}