Artificial intelligence (AI) has enabled a new paradigm of smart applications
-- changing our way of living entirely. Many of these AI-enabled applications
have very stringent latency requirements, especially for applications on mobile
devices (e.g., smartphones, wearable devices, and vehicles). Hence, smaller and
quantized deep neural network (DNN) models are developed for mobile devices,
which provide faster and more energy-efficient computation for mobile AI
applications. However, how AI models consume energy in a mobile device is still
unexplored. Predicting the energy consumption of these models, along with their
different applications, such as vision and non-vision, requires a thorough
investigation of their behavior using various processing sources. In this
paper, we introduce a comprehensive study of mobile AI applications considering
different DNN models and processing sources, focusing on computational resource
utilization, delay, and energy consumption. We measure the latency, energy
consumption, and memory usage of all the models using four processing sources
through extensive experiments. We explain the challenges in such investigations
and how we propose to overcome them. Our study highlights important insights,
such as how mobile AI behaves in different applications (vision and non-vision)
using CPU, GPU, and NNAPI. Finally, we propose a novel Gaussian process
regression-based general predictive energy model based on DNN structures,
computation resources, and processors, which can predict the energy for each
complete application cycle irrespective of device configuration and
application. This study provides crucial facts and an energy prediction
mechanism to the AI research community to help bring energy efficiency to
mobile AI applications.

本研究介绍了针对不同 DNN 模型和处理源的移动 AI 应用的全面研究，重点关注计算资源利用率、延迟和能量消耗。通过广泛的实验，我们测量了所有模型的延迟、能量消耗和内存使用情况，并提出了一种基于 DNN 结构、计算资源和处理器的高斯过程回归预测能量模型。此研究为 AI 研究社区提供了关键事实和能量预测机制，以帮助移动 AI 应用程序实现能源效率。

EPAM：移动 AI 的预测能耗模型

EPAM: A Predictive Energy Model for Mobile AI

The recent upsurge of diversified mobile applications, especially those
supported by Artificial Intelligence (AI), is spurring heated discussions on
the future evolution of wireless communications. While 5G is being deployed
around the world, efforts from industry and academia have started to look
beyond 5G and conceptualize 6G. We envision 6G to undergo an unprecedented
transformation that will make it substantially different from the previous
generations of wireless cellular systems. In particular, 6G will go beyond
mobile Internet and will be required to support ubiquitous AI services from the
core to the end devices of the network. Meanwhile, AI will play a critical role
in designing and optimizing 6G architectures, protocols, and operations. In
this article, we discuss potential technologies for 6G to enable mobile AI
applications, as well as AI-enabled methodologies for 6G network design and
optimization. Key trends in the evolution to 6G will also be discussed.

本文讨论了 6G 的潜在技术以支持移动人工智能应用，并探讨了能够设计和优化 6G 架构、协议和运营的 AI 启用方法。