Deep neural networks (DNNs) have achieved unprecedented success in the field
of artificial intelligence (AI), including computer vision, natural language
processing and speech recognition. However, their superior performance comes at
the considerable cost of computational complexity, which greatly hinders their
applications in many resource-constrained devices, such as mobile phones and
Internet of Things (IoT) devices. Therefore, methods and techniques that are
able to lift the efficiency bottleneck while preserving the high accuracy of
DNNs are in great demand in order to enable numerous edge AI applications. This
paper provides an overview of efficient deep learning methods, systems and
applications. We start from introducing popular model compression methods,
including pruning, factorization, quantization as well as compact model design.
To reduce the large design cost of these manual solutions, we discuss the
AutoML framework for each of them, such as neural architecture search (NAS) and
automated pruning and quantization. We then cover efficient on-device training
to enable user customization based on the local data on mobile devices. Apart
from general acceleration techniques, we also showcase several task-specific
accelerations for point cloud, video and natural language processing by
exploiting their spatial sparsity and temporal/token redundancy. Finally, to
support all these algorithmic advancements, we introduce the efficient deep
learning system design from both software and hardware perspectives.

本文概述了高效深度学习的方法、系统和应用，包括流行的模型压缩方法、自动化模型设计和用户自定义的基于设备的训练，以及各种针对特定任务和空间 - 时间冗余的加速技术和系统设计。