The execution of large deep neural networks (DNN) at mobile edge devices requires considerable consumption of critical resources, such as energy, while imposing demands on hardware capabilities. In approaches based on edge computing the execution of the models is offloaded to a compute-capable device positioned at the edge of 5G infrastructures. The main issue of the latter class of approaches is the need to transport information-rich signals over wireless links with limited and time-varying capacity. The recent split computing paradigm attempts to resolve this impasse by distributing the execution of DNN models across the layers of the systems to reduce the amount of data to be transmitted while imposing minimal computing load on mobile devices. In this context, we propose a novel split computing approach based on slimmable ensemble encoders. The key advantage of our design is the ability to adapt computational load and transmitted data size in real-time with minimal overhead and time. This is in contrast with existing approaches, where the same adaptation requires costly context switching and model loading. Moreover, our model outperforms existing solutions in terms of compression efficacy and execution time, especially in the context of weak mobile devices. We present a comprehensive comparison with the most advanced split computing solutions, as well as an experimental evaluation on GPU-less devices.

翻译：大型深度神经网络在移动边缘设备上的执行需要消耗大量关键资源（如能量），同时对硬件能力提出较高要求。在基于边缘计算的方法中，模型执行被卸载至位于5G基础设施边缘的具备计算能力的设备上。这类方法的主要问题在于：需要通过容量有限且时变的无线链路传输富含信息的信号。最新提出的拆分计算范式试图通过将DNN模型执行分布到系统各层来解决这一困境——既降低待传输数据量，又对移动设备施加最小计算负载。在此背景下，我们提出一种基于可调宽集成编码器的新型拆分计算方法。本设计的关键优势在于能够以极低的额外开销和时间成本实时调整计算负载与传输数据规模。这与现有方法形成鲜明对比，后者实现相同适应性需要昂贵的上下文切换和模型加载开销。此外，我们的模型在压缩效率和执行时间方面均优于现有方案，尤其在弱移动设备场景下表现突出。我们与最先进的拆分计算方案进行了全面比较，并在无GPU设备上完成了实验评估。