Although mission-critical applications require the use of deep neural networks (DNNs), their continuous execution at mobile devices results in a significant increase in energy consumption. While edge offloading can decrease energy consumption, erratic patterns in channel quality, network and edge server load can lead to severe disruption of the system's key operations. An alternative approach, called split computing, generates compressed representations within the model (called "bottlenecks"), to reduce bandwidth usage and energy consumption. Prior work has proposed approaches that introduce additional layers, to the detriment of energy consumption and latency. For this reason, we propose a new framework called BottleFit, which, in addition to targeted DNN architecture modifications, includes a novel training strategy to achieve high accuracy even with strong compression rates. We apply BottleFit on cutting-edge DNN models in image classification, and show that BottleFit achieves 77.1% data compression with up to 0.6% accuracy loss on ImageNet dataset, while state of the art such as SPINN loses up to 6% in accuracy. We experimentally measure the power consumption and latency of an image classification application running on an NVIDIA Jetson Nano board (GPU-based) and a Raspberry PI board (GPU-less). We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w.r.t.) local computing and by 37% and 55% w.r.t. edge offloading. We also compare BottleFit with state-of-the-art autoencoders-based approaches, and show that (i) BottleFit reduces power consumption and execution time respectively by up to 54% and 44% on the Jetson and 40% and 62% on Raspberry PI; (ii) the size of the head model executed on the mobile device is 83 times smaller. The code repository will be published for full reproducibility of the results.

翻译：虽然任务关键应用程序需要使用深神经网络(DNNS),但是在移动设备上持续执行40个功能,导致能源消耗大幅增加。虽然边缘卸载可以减少能源消耗,但55个频道质量、网络和边缘服务器负荷的不稳定模式可能导致系统关键操作严重中断。另一种方法,称为分裂计算,在模型中生成压缩表示(称为“瓶颈”),以减少带宽使用和能源消耗。先前的工作提议了一些方法,引入更多的层,损害能源消耗和延缓。为此,我们提议了一个名为BottleFit的新框架,除了针对DNNE的架构修改之外,还包括一个新颖的培训战略,以达到高精度能源消耗,甚至强大的压缩速度。我们在图像分类中对尖端的 DNNNNE模型应用了BFit Fitt, 在图像网络数据库中实现了77.1%的数据压缩,其精确度损失高达0.6%,而SPINNN的状态,在精确度上下降到了6%。我们实验性地测量了BOIFO的耗耗耗耗能和图像分类应用程序,在49 GOI的节节节节节节节上的耗将分别显示PIG的耗耗为0.%。