Processing visual data on mobile devices has many applications, e.g.,
emergency response and tracking. State-of-the-art computer vision techniques
rely on large Deep Neural Networks (DNNs) that are usually too power-hungry to
be deployed on resource-constrained edge devices. Many techniques improve the
efficiency of DNNs by using sparsity or quantization. However, the accuracy and
efficiency of these techniques cannot be adapted for diverse edge applications
with different hardware constraints and accuracy requirements. This paper
presents a novel technique to allow DNNs to adapt their accuracy and energy
consumption during run-time, without the need for any re-training. Our
technique called AdaptiveActivation introduces a hyper-parameter that controls
the output range of the DNNs' activation function to dynamically adjust the
sparsity and precision in the DNN. AdaptiveActivation can be applied to any
existing pre-trained DNN to improve their deployability in diverse edge
environments. We conduct experiments on popular edge devices and show that the
accuracy is within 1.5% of the baseline. We also show that our approach
requires 10%--38% less memory than the baseline techniques leading to more
accuracy-efficiency tradeoff options

移动设备上处理视觉数据具有多种应用，但现有的计算机视觉技术通常过于耗电无法部署在资源受限的边缘设备上。本文提出一种新技术，通过引入超参数来动态调整深度神经网络的精确度和能源消耗，无需重新训练即可适应不同边缘应用的硬件约束与精确度要求。该技术称为 AdaptiveActivation，实验证明其在流行的边缘设备上可以达到基准的误差范围内，并且比基准技术所需内存减少了 10% 到 38%，从而提供更多的精确度与效率权衡选项。