Embedded Artificial Intelligence: Devices, Embedded Systems, and Industrial Applications

✍ Scribed by Ovidiu Vermesan, Mario Diaz Nava, Björn Debaillie

• Publisher: River Publishers
• Year: 2023
• Tongue: English
• Leaves: 143
• Series: River Publishers Series in Communications and Networking
• Category: Library

✦ Synopsis

Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-efficient hardware devices for processing information at the network edge.

Embedded AI combines embedded machine learning (ML) and deep learning (DL) based on neural networks (NN) architectures such as convolutional NN (CNN), or spiking neural network (SNN) and algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources.

Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations.

This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO.

The book’s content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Acknowledgement
Table of Contents
Preface
Editors Biography
List of Figures
List of Tables
Chapter 1: Power Optimized Wafermap Classification for Semiconductor Process Monitoring
Chapter 2: Low-power Analog In-memory Computing Neuromorphic Circuits
Chapter 3: Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators
Chapter 4: Low-Power Vertically Stacked One Time Programmable Multi-bit IGZO-Based BEOL Compatible Ferroelectric TFT Memory Devices with Lifelong Retention for Monolithic 3D-Inference Engine Applications
Chapter 5: Generating Trust in Hardware through Physical Inspection
Chapter 6: Meeting the Latency and Energy Constraints on Timing-critical Edge-AI Systems
Chapter 7: Sub-mW Neuromorphic SNN Audio Processing Applications with Rockpool and Xylo
Chapter 8: An Embedding Workflow for Tiny Neural Networks on Arm Cortex-M0(+) Cores
Chapter 9: Edge AI Platforms for Predictive Maintenance in Industrial Applications
Chapter 10: Food Ingredients Recognition Through Multi-label Learning
Index