On the fly Deep Neural Network Optimization Control for Low-Power
  Computer Vision
Ishmeet Kaur Adwaita Janardhan Jadhav
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