TO DEVELOP THE NOBEL PRIZE “FOR FOUNDATIONAL

Transcript:TO DEVELOP THE NOBEL PRIZE “FOR FOUNDATIONAL:
TO DEVELOP THE NOBEL PRIZE “FOR FOUNDATIONAL DISCOVERIES AND INVENTIONS THAT ENABLE MACHINE LEARNING WITH ARTIFICIAL NEURAL NETWORKS ” THEORY BY HARDWARE DESCRIPTION LANGUAGE BY ER. SATYENDRA PRASAD RAJGOND director.gitarc.tarc@gmail.com DIRECTOR_TECHNOLOGY & RESEARCH CENTRE GONDWANA INTERNATIONAL TECHNOLOGY & RESEARCH CENTRE (GITARC) BHATPAR RANI, [INDIA] INTERNATIONAL PRINCIPAL AUTHOR (Author ID: Sci50161223) IETE NATIONAL INDIA , INDIA REPRESENTATOR, NCC-IP AICTE GOVERNMENT OF INDIA, INTERNATIONAL VERILOG DEVELOPER, INTERNATIONAL TECHNOLOGY DEVELOPER, INTERNATIONAL MATHWORK DEVELOPER ,INTERNATIONAL THESIS DEVELOPER, THE NOBEL PRIZE THEORY DEVELOPER, INTERNATIONAL TEXAS INSTRUMENTS DEVELOPER (USA),INTERNATIONAL TELECOMMUNICATION UNION (GENEWA), IEEE INTERNATIONAL (U.S.A.) , S.A.E. INTERNATIONAL (U.S.A.), GUINNESS WORLD RECORD LONDON, GOLD MEDALIST, INTERNATIONAL AWARD WINNER, INTERNATIONAL BRAND AMBASSADOR 8th CAPCDR International Conference on " Artificial Intelligence and Technology in Academia and Profession", December 25-26, 2024. GITARC BHATPAR RANI INDIA T & R INTRODUCTION LITERATURE REVIEW RESEARCH GAPS MATERIAL AND METHODS RESULTS SOFTWARE IMPLEMENTATION DISCUSSION CONCLUSION REFERENCES OUTLINE INTRODUCTION Machine Learning Machine learning (ML) has emerged as a transformative field, enabling computers to learn from data and make predictions or decisions without explicit programming. Rooted in statistics and computer science, ML encompasses a variety of algorithms and models, with artificial neural networks (ANNs) gaining prominence due to their ability to capture complex patterns in large datasets. The advent of big data and increased computational power has fueled the rapid growth of ML applications across diverse sectors, including healthcare, finance, and autonomous systems. The integration of hardware description languages (HDLs) into ML research has opened new avenues for optimizing neural network architectures at the hardware level. By using HDLs like VHDL and Verilog, researchers can design and implement ANNs more efficiently, facilitating advancements in parallel processing and field-programmable gate arrays (FPGAs). This paper investigates the foundational theories and innovations that bridge the gap between software algorithms and hardware architectures, emphasizing the importance of hardware-software co-design. As ML continues to evolve, understanding the interplay between these domains is crucial for enhancing performance and scalability in artificial intelligence applications . (Jordan & Mitchell, 2015; Suda et al., 2016). Artificial Neural Networks Artificial Neural Networks (ANNs) are computational models inspired by the biological neural networks that constitute the human brain. These models consist of interconnected nodes or neurons, organized in layers, which process and learn from data through adjustments in connection weights. ANNs have gained significant attention in recent years due to their remarkable capabilities in tasks such as image recognition, natural language processing, and predictive analytics. The