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