  Deep neural networks (DNNs) are state-of-the-art techniques for solving most
computer vision problems. DNNs require billions of parameters and operations to
achieve state-of-the-art results. This requirement makes DNNs extremely
compute, memory, and energy-hungry, and consequently difficult to deploy on
small battery-powered Internet-of-Things (IoT) devices with limited computing
resources. Deployment of DNNs on Internet-of-Things devices, such as traffic
cameras, can improve public safety by enabling applications such as automatic
accident detection and emergency response.Through this paper, we survey the
recent advances in low-power and energy-efficient DNN implementations that
improve the deployability of DNNs without significantly sacrificing accuracy.
In general, these techniques either reduce the memory requirements, the number
of arithmetic operations, or both. The techniques can be divided into three
major categories: neural network compression, network architecture search and
design, and compiler and graph optimizations. In this paper, we survey both
low-power techniques for both convolutional and transformer DNNs, and summarize
the advantages, disadvantages, and open research problems.
