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Название: Advancing VLSI through Machine Learning: Innovations and Research Perspectives
Автор: Abhishek Narayan Tripathi, Jagana Bihari Padhy, Indrasen Singh, Shubham Tayal, Ghanshyam Singh
Издательство: CRC Press
Серия: Materials, Devices, and Circuits
Год: 2025
Страниц: 267
Язык: английский
Формат: pdf (true), epub
Размер: 40.8 MB

This book explores the synergy between very large-scale integration (VLSI) and Machine Learning (ML) and its applications across various domains. It investigates how ML techniques can enhance the design and testing of VLSI circuits, improve power efficiency, optimize layouts, and enable novel architectures.

This book bridges the gap between VLSI and ML, showcasing the potential of this integration in creating innovative electronic systems, advancing computing capabilities, and paving the way for a new era of intelligent devices and technologies. Additionally, it covers how VLSI technologies can accelerate ML algorithms, enabling more efficient and powerful data processing and inference engines. It explores both hardware and software aspects, covering topics like hardware accelerators, custom hardware for specific ML tasks, and ML-driven optimization techniques for chip design and testing.

Matrix multiplication is a fundamental operation in Machine Learning algorithms, forming the backbone of many computational tasks such as neural network training and inference. As the demand for efficient and high-performance computing systems in Machine Learning continues to rise, exploring field-programmable gate array (FPGA) architecture designs for accelerating matrix multiplication has garnered significant interest. This introduction provides an overview of the importance of matrix multiplication in Machine Learning, the challenges associated with traditional computing architectures, and the potential benefits of leveraging FPGA-based solutions. Machine Learning algorithms often involve large-scale matrix operations, including matrix-vector products, matrix-matrix products, and convolutions. Matrix multiplication is particularly prevalent in Deep Learning models, where layers of neurons are interconnected through weighted connections represented as matrices. Efficient matrix multiplication is crucial for accelerating training and inference tasks, enabling faster model convergence and improved performance.

This book will be helpful for academicians, researchers, postgraduate students, and those working in ML-driven VLSI.

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