  As deep neural networks continue to expand and become more complex, most edge
devices are unable to handle their extensive processing requirements.
Therefore, the concept of distributed inference is essential to distribute the
neural network among a cluster of nodes. However, distribution may lead to
additional energy consumption and dependency among devices that suffer from
unstable transmission rates. Unstable transmission rates harm real-time
performance of IoT devices causing low latency, high energy usage, and
potential failures. Hence, for dynamic systems, it is necessary to have a
resilient DNN with an adaptive architecture that can downsize as per the
available resources. This paper presents an empirical study that identifies the
connections in ResNet that can be dropped without significantly impacting the
model's performance to enable distribution in case of resource shortage. Based
on the results, a multi-objective optimization problem is formulated to
minimize latency and maximize accuracy as per available resources. Our
experiments demonstrate that an adaptive ResNet architecture can reduce shared
data, energy consumption, and latency throughout the distribution while
maintaining high accuracy.
