Integrating Neural Networks and VLSI Design Education: A Multidisciplinary Approach for Advanced Technical Learning in Engineering - Omar J. Alkhatib, Dr Satti Sudha Mohan Reddy, Dr. J. Ramesh Babu; Dimple Saproo (Journal of Daoist Studies, 2026)
The increasing convergence of artificial intelligence and hardware design has created a demand for innovative educational frameworks that equip engineering students with both algorithmic intelligence and practical implementation skills. This research paper explores a multidisciplinary approach to integrating neural networks and VLSI design education, aiming to bridge the gap between theoretical machine learning concepts and hardware-level realization. Traditional engineering curricula often treat these domains in isolation, limiting students’ ability to understand how computational models are translated into efficient silicon architectures. The proposed approach emphasizes a cohesive learning model that combines neural network theory, digital design principles, and hardware description techniques within a unified pedagogical structure. By incorporating hands-on design experiences, simulation tools, and project-based learning modules, the study demonstrates how students can develop a deeper understanding of neural network architectures while simultaneously gaining proficiency in VLSI implementation. The framework encourages the use of hardware description languages and FPGA-based prototyping to enable learners to design, simulate, and test neural network models in real-time environments. Additionally, the integration of emerging topics such as neuromorphic computing and hardware acceleration provides students with exposure to cutting-edge technological advancements. The research also evaluates the effectiveness of this multidisciplinary approach through academic performance indicators, student feedback, and practical project outcomes, revealing improvements in conceptual clarity, problem-solving abilities, and interdisciplinary thinking. Challenges such as curriculum design complexity, resource availability, and the need for faculty expertise are also addressed, with recommendations for scalable implementation in engineering institutions. The findings suggest that integrating neural networks with VLSI design education not only enhances technical competency but also prepares students for industry demands in areas such as embedded systems, AI hardware development, and high-performance computing. This study contributes to the evolving landscape of engineering education by proposing a structured and application-oriented model that fosters innovation, adaptability, and advanced technical learning in a rapidly changing technological environment.