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.

本文通过实证研究，鉴定了ResNet中可舍弃的连接，以实现在资源不足情况下的分布式计算；同时，提出了一个多目标优化问题，旨在减少延迟、提高准确性；实验证明了自适应的ResNet架构可以在分布过程中减少共享数据、能耗和延迟，同时保持高准确性。

适应性ResNet架构用于资源受限的物联网系统中的分布式推理