For various reasons including those related to climate changes, {\em energy}
has become a critical concern in all relevant activities and technical designs.
For the specific case of computer activities, the problem is exacerbated with
the emergence and pervasiveness of the so called {\em intelligent devices}.
From the application side, we point out the special topic of {\em Artificial
Intelligence}, who clearly needs an efficient computing support in order to
succeed in its purpose of being a {\em ubiquitous assistant}. There are mainly
two contexts where {\em energy} is one of the top priority concerns: {\em
embedded computing} and {\em supercomputing}. For the former, power consumption
is critical because the amount of energy that is available for the devices is
limited. For the latter, the heat dissipated is a serious source of failure and
the financial cost related to energy is likely to be a significant part of the
maintenance budget. On a single computer, the problem is commonly considered
through the electrical power consumption. This paper, written in the form of a
survey, we depict the landscape of energy concerns in computer activities, both
from the hardware and the software standpoints.

能源、智能设备、人工智能、嵌入式计算和超级计算是该研究论文中的关键词，并从硬件和软件的角度描述了计算活动中与能源相关的问题。

高性能计算系统和应用的能源问题

Energy Concerns with HPC Systems and Applications

In this paper, we propose a novel network design mechanism for efficient
embedded computing. Inspired by the limited computing patterns, we propose to
fix the number of channels in a group convolution, instead of the existing
practice that fixing the total group numbers. Our solution based network, named
Variable Group Convolutional Network (VarGNet), can be optimized easier on
hardware side, due to the more unified computing schemes among the layers.
Extensive experiments on various vision tasks, including classification,
detection, pixel-wise parsing and face recognition, have demonstrated the
practical value of our VarGNet.

本文提出了一种新的嵌入式计算网络设计机制，名为 VarGNet，它基于固定通道数的组卷积，使得网络更容易在硬件端进行优化，针对分类、检测、像素级解析和人脸识别等各种视觉任务的广泛实验证明了 VarGNet 的实用价值。