  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.
