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%.

翻译：深度神经网络（DNN）的最新进展源于其在不同领域的卓越表现。然而，其固有的庞大体积阻碍了这些网络在边缘设备、移动端和物联网平台等资源受限设备上的部署。为此，学界提出了多种策略，从部分云端计算任务卸载（拆分计算）到在DNN层内集成早期退出机制。本研究提出了一种创新性统一方法，将早期退出与拆分计算相结合。我们确定了“拆分层”（即边缘设备计算的最优DNN深度），并基于准确率、计算效率与通信成本，决定是在边缘设备上进行推理，还是将其卸载至云端。此外，图像分类面临多种环境失真问题，这些失真受时间、光照和天气等因素影响。为适应此类失真，我们提出了I-SplitEE算法——一种适用于缺乏真实标签数据且数据顺序到达场景的在线无监督算法。通过在经受不同失真处理的Caltech-256和Cifar-10数据集上的实验验证，结果显示I-SplitEE能够将成本降低至少55%，同时性能退化幅度不超过5%。