Embedded artificial intelligence : devices, embedded systems, and industrial applications

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...

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Bibliographic Details
Other Authors: Vermesan, Ovidiu, (Editor), Nava, Mario Diaz, (Editor), Debaillie, Bjørn. (Editor)
Format: eBook
Language: English
Published: [United States] : River Publishers, [2022]
Series: River Publishers series in communications and networking.
Subjects:
Embedded computer systems.
Artificial intelligence.
Edge computing.
elektronické knihy
electronic books
ISBN: 9788770228206
8770228205
9781003394440
1003394442
9781000881912
1000881911
1000882039
9781000882032
9788770228213
Physical Description: 1 online resource.

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245 0 0 |a Embedded artificial intelligence :  |b devices, embedded systems, and industrial applications /  |c editors, Ovidiu Vermesan, Mario Diaz Nava, Bjørn Debaillie. 
264 1 |a [United States] :  |b River Publishers,  |c [2022] 
264 4 |c ©2022 
300 |a 1 online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a River Publishers series in communications and networking 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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 or spiking neural network (SNN) 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 book⁰́₉s 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. 
505 0 |a Preface ix Editors Biography xiii List of Figures xv List of Tables xxiii 1. Power Optimized Wafermap Classification for Semiconductor Process Monitoring 1 2. Low-power Analog In-memory Computing Neuromorphic Circuits 15 3. Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators 25 4. Low-Power Vertically Stacked One Time Programmable Multi-bit IGZO-Based BEOL Compatible Ferroelectric TFT Memory Devices with Lifelong Retention for Monolithic 3DInference Engine Applications 37 5. Generating Trust in Hardware through Physical Inspection 45 6. Meeting the Latency and Energy Constraints on Timingcritical Edge-AI Systems 61 7. Sub-mW Neuromorphic SNN Audio Processing Applications with Rockpool and Xylo 69 8. An Embedding Workflow for Tiny Neural Networks on Arm Cortex-M0 Cores 79 9. Edge AI Platforms for Predictive Maintenance in Industrial Applications 89 10. Food Ingredients Recognition Through Multi-label Learning 105 Index 117. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Embedded computer systems. 
650 0 |a Artificial intelligence. 
650 0 |a Edge computing. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Vermesan, Ovidiu,  |e editor. 
700 1 |a Nava, Mario Diaz,  |e editor. 
700 1 |a Debaillie, Bjørn.  |e editor. 
830 0 |a River Publishers series in communications and networking. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpEAIDESIB/embedded-artificial-intelligence?kpromoter=marc  |y Full text 

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