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.

通过本文，我们调查了低功耗和能效更高的深度神经网络实现的最新进展，其提高了深度神经网络的可部署性而在不显著牺牲准确性的情况下。这些技术可以分为三个主要类别：神经网络压缩、网络架构搜索和设计、以及编译器和图优化。我们调查了卷积和变换器深度神经网络的低功耗技术，并总结了其优点、缺点和研究中的问题。