  The recent advances in Deep Neural Networks (DNNs) stem from their
exceptional performance across various domains. However, their inherent large
size hinders deploying these networks on resource-constrained devices like
edge, mobile, and IoT platforms. Strategies have emerged, from partial cloud
computation offloading (split computing) to integrating early exits within DNN
layers. Our work presents an innovative unified approach merging early exits
and split computing. We determine the 'splitting layer', the optimal depth in
the DNN for edge device computations, and whether to infer on edge device or be
offloaded to the cloud for inference considering accuracy, computational
efficiency, and communication costs. Also, Image classification faces diverse
environmental distortions, influenced by factors like time of day, lighting,
and weather. To adapt to these distortions, we introduce I-SplitEE, an online
unsupervised algorithm ideal for scenarios lacking ground truths and with
sequential data. Experimental validation using Caltech-256 and Cifar-10
datasets subjected to varied distortions showcases I-SplitEE's ability to
reduce costs by a minimum of 55% with marginal performance degradation of at
most 5%.
