Articoli correlati a Harnessing the Potential of Deep-learning Algorithms...
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification - Brossura
Compra nuovo
Visualizza questo articolo
EUR 80,40
EUR 7,64 per la spedizione da U.S.A. a Italia
Destinazione, tempi e costiRisultati della ricerca per Harnessing the Potential of Deep-learning Algorithms...
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification (Foundations and Trends(r) in Engineering)
Da: California Books, Miami, FL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo I-9781638285380
Quantità: Più di 20 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification
Print on DemandDa: THE SAINT BOOKSTORE, Southport, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 181. Codice articolo C9781638285380
Quantità: Più di 20 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification (Foundations and Trends® in Engineering)
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: Brand New. 120 pages. 6.14x0.26x9.21 inches. In Stock. Codice articolo x-1638285381
Quantità: 2 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification (Paperback)
Editore:
now publishers Inc, Hanover, 2025
ISBN 10: 1638285381
ISBN 13: 9781638285380
Nuovo
Paperback
Da: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. Recent developments in semiconductor technology have significantly increased integration density, with modern System-on-Chip (SoC) designs for high-performance computing now exceeding 10 billion transistors. However, the traditional CMOS transistor scaling law has reached its physical limits. This has necessitated the adoption of More-than-Moore (MtM) technology, which integrates novel design methodologies and heterogeneous computing architectures. Electronic Design Automation (EDA), design reuse, and IP-based methodologies have been instrumental in bridging the productivity gap, reducing time-to-market, and meeting increasingly stringent performance and security requirements. As electronic systems grow in complexity, new design and verification methodologies are emerging to address these challenges effectively.In the pre-Internet of Things (IoT) era, the security of SoC designs was often an afterthought. However, the widespread adoption of IoT devices has made security a critical concern at every level of deployment. Current EDA tools, while optimizing for performance, inadvertently introduce vulnerabilities that expose circuits to threats such as side-channel attacks, reverse engineering, and hardware Trojans. This increasing focus on security necessitates the development of security-aware EDA tools capable of addressing threats such as fault injection, information leakage, and timing and power-based attacks.Deep learning algorithms and Large Language Models (LLMs) have emerged as promising solutions to address the growing challenges in SoC and chiplet design and verification. The application of AI-driven methodologies is revolutionizing Register Transfer Logic (RTL) generation, hardware security, and design automation, leading to enhanced power-performance-area (PPA) metrics and improved verification efficiency. This special issue features three works that explore the transformative role of AI in EDA and SoC design, namely Deep Learning and Generative AI for Monolithic and Chiplet SoC Design and Verification, Large Language Models for EDA, and Evaluating LLMs for Automatic RTL Generation for Combinational Circuits via High-Level Synthesis. These works collectively underscore the transformative potential of AI and LLMs in revolutionizing SoC design and EDA workflows. This special issue provides valuable insights for researchers and industry professionals, inspiring further advancements in AI-assisted hardware design and verification. This special issue explores the transformative role of AI in EDA and SoC design. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9781638285380
Quantità: 1 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification (Paperback)
Editore:
now publishers Inc, Hanover, 2025
ISBN 10: 1638285381
ISBN 13: 9781638285380
Nuovo
Paperback
Da: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. Recent developments in semiconductor technology have significantly increased integration density, with modern System-on-Chip (SoC) designs for high-performance computing now exceeding 10 billion transistors. However, the traditional CMOS transistor scaling law has reached its physical limits. This has necessitated the adoption of More-than-Moore (MtM) technology, which integrates novel design methodologies and heterogeneous computing architectures. Electronic Design Automation (EDA), design reuse, and IP-based methodologies have been instrumental in bridging the productivity gap, reducing time-to-market, and meeting increasingly stringent performance and security requirements. As electronic systems grow in complexity, new design and verification methodologies are emerging to address these challenges effectively.In the pre-Internet of Things (IoT) era, the security of SoC designs was often an afterthought. However, the widespread adoption of IoT devices has made security a critical concern at every level of deployment. Current EDA tools, while optimizing for performance, inadvertently introduce vulnerabilities that expose circuits to threats such as side-channel attacks, reverse engineering, and hardware Trojans. This increasing focus on security necessitates the development of security-aware EDA tools capable of addressing threats such as fault injection, information leakage, and timing and power-based attacks.Deep learning algorithms and Large Language Models (LLMs) have emerged as promising solutions to address the growing challenges in SoC and chiplet design and verification. The application of AI-driven methodologies is revolutionizing Register Transfer Logic (RTL) generation, hardware security, and design automation, leading to enhanced power-performance-area (PPA) metrics and improved verification efficiency. This special issue features three works that explore the transformative role of AI in EDA and SoC design, namely Deep Learning and Generative AI for Monolithic and Chiplet SoC Design and Verification, Large Language Models for EDA, and Evaluating LLMs for Automatic RTL Generation for Combinational Circuits via High-Level Synthesis. These works collectively underscore the transformative potential of AI and LLMs in revolutionizing SoC design and EDA workflows. This special issue provides valuable insights for researchers and industry professionals, inspiring further advancements in AI-assisted hardware design and verification. This special issue explores the transformative role of AI in EDA and SoC design. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781638285380
Quantità: 1 disponibili
Immagini fornite dal venditore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification
Print on DemandDa: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This special issue explores the transformative role of AI in EDA and SoC design. Codice articolo 9781638285380
Quantità: 2 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification
Da: Books Puddle, New York, NY, U.S.A.
Valutazione del venditore 4 su 5 stelle
Condizione: New. Codice articolo 26404328376
Quantità: 4 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification
Print on DemandDa: Majestic Books, Hounslow, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. Print on Demand. Codice articolo 409874535
Quantità: 4 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification (Paperback)
Editore:
now publishers Inc, Hanover, 2025
ISBN 10: 1638285381
ISBN 13: 9781638285380
Nuovo
Paperback
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. Recent developments in semiconductor technology have significantly increased integration density, with modern System-on-Chip (SoC) designs for high-performance computing now exceeding 10 billion transistors. However, the traditional CMOS transistor scaling law has reached its physical limits. This has necessitated the adoption of More-than-Moore (MtM) technology, which integrates novel design methodologies and heterogeneous computing architectures. Electronic Design Automation (EDA), design reuse, and IP-based methodologies have been instrumental in bridging the productivity gap, reducing time-to-market, and meeting increasingly stringent performance and security requirements. As electronic systems grow in complexity, new design and verification methodologies are emerging to address these challenges effectively.In the pre-Internet of Things (IoT) era, the security of SoC designs was often an afterthought. However, the widespread adoption of IoT devices has made security a critical concern at every level of deployment. Current EDA tools, while optimizing for performance, inadvertently introduce vulnerabilities that expose circuits to threats such as side-channel attacks, reverse engineering, and hardware Trojans. This increasing focus on security necessitates the development of security-aware EDA tools capable of addressing threats such as fault injection, information leakage, and timing and power-based attacks.Deep learning algorithms and Large Language Models (LLMs) have emerged as promising solutions to address the growing challenges in SoC and chiplet design and verification. The application of AI-driven methodologies is revolutionizing Register Transfer Logic (RTL) generation, hardware security, and design automation, leading to enhanced power-performance-area (PPA) metrics and improved verification efficiency. This special issue features three works that explore the transformative role of AI in EDA and SoC design, namely Deep Learning and Generative AI for Monolithic and Chiplet SoC Design and Verification, Large Language Models for EDA, and Evaluating LLMs for Automatic RTL Generation for Combinational Circuits via High-Level Synthesis. These works collectively underscore the transformative potential of AI and LLMs in revolutionizing SoC design and EDA workflows. This special issue provides valuable insights for researchers and industry professionals, inspiring further advancements in AI-assisted hardware design and verification. This special issue explores the transformative role of AI in EDA and SoC design. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781638285380
Quantità: 1 disponibili
Foto dell'editore
Harnessing the Potential of Deep-learning Algorithms and Generative AI for SoC and Chiplet Design and Verification
Print on DemandDa: Biblios, Frankfurt am main, HESSE, Germania
Valutazione del venditore 4 su 5 stelle
Condizione: New. PRINT ON DEMAND. Codice articolo 18404328370
Quantità: 4 disponibili