Structural health monitoring (SHM) tasks like damage detection are crucial
for decision-making regarding maintenance and deterioration. For example, crack
detection in SHM is crucial for bridge maintenance as crack progression can
lead to structural instability. However, most AI/ML models in the literature
have low latency and late inference time issues while performing in real-time
environments. This study aims to explore the integration of edge-AI in the SHM
domain for real-time bridge inspections. Based on edge-AI literature, its
capabilities will be valuable integration for a real-time decision support
system in SHM tasks such that real-time inferences can be performed on physical
sites. This study will utilize commercial edge-AI platforms, such as Google
Coral Dev Board or Kneron KL520, to develop and analyze the effectiveness of
edge-AI devices. Thus, this study proposes an edge AI framework for the
structural health monitoring domain. An edge-AI-compatible deep learning model
is developed to validate the framework to perform real-time crack
classification. The effectiveness of this model will be evaluated based on its
accuracy, the confusion matrix generated, and the inference time observed in a
real-time setting.

本研究旨在探索将边缘人工智能（AI）与结构健康监测领域相结合，例如在实时桥梁检查任务中，将商用的 Google Coral Dev Board 或 Kneron KL520 等边缘 AI 平台应用于决策支持系统，以便在物理现场上执行实时推断，从而进行其准确性、混淆矩阵生成和推理时间的评估。

在结构健康监控领域集成边缘 AI 技术

Integrating Edge-AI in Structural Health Monitoring domain

Low-Power Edge-AI capabilities are essential for on-device extended reality
(XR) applications to support the vision of Metaverse. In this work, we
investigate two representative XR workloads: (i) Hand detection and (ii) Eye
segmentation, for hardware design space exploration. For both applications, we
train deep neural networks and analyze the impact of quantization and hardware
specific bottlenecks. Through simulations, we evaluate a CPU and two systolic
inference accelerator implementations. Next, we compare these hardware
solutions with advanced technology nodes. The impact of integrating
state-of-the-art emerging non-volatile memory technology (STT/SOT/VGSOT MRAM)
into the XR-AI inference pipeline is evaluated. We found that significant
energy benefits (>=24%) can be achieved for hand detection (IPS=10) and eye
segmentation (IPS=0.1) by introducing non-volatile memory in the memory
hierarchy for designs at 7nm node while meeting minimum IPS (inference per
second). Moreover, we can realize substantial reduction in area (>=30%) owing
to the small form factor of MRAM compared to traditional SRAM.

本研究旨在探索面向元宇宙应用的低功耗边缘人工智能能力，通过对手势识别和眼部分割工作负载的研究，以量化和硬件特定瓶颈的影响，评估了不同的硬件设计和不同技术节点，并引入新型的非挥发性存储器技术，证明了能够在满足最低 IPS 的情况下实现能耗显著的降低（> = 24%），同时区域显着缩小（> = 30％）。

面向 XR 应用的边缘 AI 硬件的基于内存的设计空间探索

Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications

The 5G network connecting billions of Internet-of-Things (IoT) devices will
make it possible to harvest an enormous amount of real-time mobile data.
Furthermore, the 5G virtualization architecture will enable cloud computing at
the (network) edge. The availability of both rich data and computation power at
the edge has motivated Internet companies to deploy artificial intelligence
(AI) there, creating the hot area of edge-AI. Edge learning, the theme of this
project, concerns training edge-AI models, which endow on IoT devices
intelligence for responding to real-time events. However, the transmission of
high-dimensional data from many edge devices to servers can result in excessive
communication latency, creating a bottleneck for edge learning. Traditional
wireless techniques deigned for only radio access are ineffective in tackling
the challenge. Attempts to overcome the communication bottleneck has led to the
development of a new class of techniques for intelligent radio resource
management (RRM), called data-importance aware RRM. Their designs feature the
interplay of active machine learning and wireless communication. Specifically,
the metrics that measure data importance in active learning (e.g.,
classification uncertainty and data diversity) are applied to RRM for efficient
acquisition of distributed data in wireless networks to train AI models at
servers. This article aims at providing an introduction to the emerging area of
importance-aware RRM. To this end, we will introduce the design principles,
survey recent advancements in the area, discuss some design examples, and
suggest some promising research opportunities.

本文主要介绍了一个名为数据重要性感知无线资源管理的新技术，通过将主动学习中用于度量数据重要性的指标应用于无线资源管理以进行有效的分布式数据获取来解决边缘学习中的通信瓶颈问题。该技术通过 5G 网络连接数十亿的物联网设备，实现边缘计算并部署人工智能产生了广泛应用。