AI Processors: Fundamental Principles of Design and Functionality
As artificial intelligence continues to reshape the technology landscape, AI technology transformation due to processors has become a driving force. Unlike traditional chips, processors purpose-built to handle AI workloads are engineered to meet the unique demands of machine learning and deep learning applications. This course provides a foundational understanding of underlying AI-processor design principles, exploring how these specialized architectures are optimized. Learners will examine AI processors vs conventional architectures, gaining insight into how these two paradigms differ in structure, functionality, and performance. To deepen this comprehension, the course delves into the mathematical, electrical, and computational foundations of AI processing, illuminating the core concepts that enable intelligent computation. By the end of the course, learners will have developed a framework for analyzing and understanding AI processors and their role in advanced computing systems, equipping them to critically assess emerging technologies and contribute to the future of AI-driven innovation.
What you will learn:
- Core architectural principles that distinguish AI processors from conventional computing systems.
- Fundamental computational models used in AI processing and articulate how they influence processor behavior and performance.
- Ability to analyze key processing concepts and design strategies, including dataflow, parallelism, and specialized acceleration techniques.
- Ability to apply mathematical foundations—such as linear algebra, optimization, and numerical methods—to understand AI computation at the hardware level.
- How to evaluate performance and efficiency considerations, identifying trade-offs in processor design and their implications for real-world AI workloads.
- How to develop a conceptual framework for interpreting and comparing AI processor technologies across different architectures and use cases.
This course is part of the following course program:
AI Processor Architecture, Design Principles, and Performance
Courses included in this program:
Who Should Attend: Early‑career Electrical, Computer, or Hardware Engineers seeking a structured introduction to AI‑specific processor design, Engineers transitioning from general-purpose CPU/GPU work into AI‑accelerated hardware development, Chip Design and Microarchitecture Engineers who need deeper grounding in the fundamentals before advancing to specialized architectures, Machine Learning Engineers who want to understand the hardware principles behind model execution, dataflows, and performance behavior, Embedded Systems Developers beginning to integrate AI‑capable processors into edge or IoT platforms, Systems Engineers and Technical Leads who must evaluate AI hardware options and understand architectural trade‑offs, Researchers and Graduate Students entering the fields of AI hardware, computational architectures, or intelligent systems , Product Managers and Technical Strategists responsible for planning AI‑enabled products and needing foundational literacy in processor capabilities, Professionals shifting into AI hardware ecosystems who require a clear, conceptual framework before engaging with advanced architectures, Educators and Technical Trainers developing curricula or instructional materials on AI computation and processor fundamentals.
Instructor:
Dr. Santhosh Sivasubramani
Dr. Santhosh Sivasubramani is the working group chair of IEC/IEEE P62659™, the International Standard for Nanomanufacturing in Large Scale Manufacturing for Nanoelectronics. Sivasubramani is a Senior Member of IEEE as well as a member of the IEEE Nanotechnology Council Technical Committee 12 on Nanomagnetics and the IEEE Task Force on Rebooting Computing. Sivasubramani won the IEEE Member Geographic Activities Young Professional Achievement Award in 2023. He is a contributor to the IEEE International Roadmap of Devices and Systems: Beyond CMOS Working Group Report, in addition to serving as the IEEE NTC Standards Committee Secretary. Dr. Sivasubramani gained post-Ph.D. academic and industrial experience in the Advanced Embedded Systems and IC Design Laboratory and the Nanomagnetics Research Lab, demonstrating a versatile blend of expertise. He also contributes as a Track/Session Chair for IEEE international conferences and Founder of RSL Quantum. Dr. Sivasubramani graduated with a Ph.D. in Rebooting Computing from the Indian Institute of Technology Hyderabad (IITH).
Publication Year: 2026
ISBN: 978-1-7281-7903-2