AI Processors: Understanding Neural Processing Units for Industry Deployment
This course examines the fundamental architectural paradigms, integration methodologies, and industry-specific deployment and optimization techniques that are reshaping AI processing possibilities across diverse industrial domains. The course progresses systematically from core NPU architectural foundations, through heterogeneous system integration challenges, performance profiling and validation methodologies, to comprehensive implementation frameworks for enterprise environments. The central objective of this course is to develop advanced understanding and practical skills in neural processing unit architecture, integration, and deployment strategies for specialized industrial applications across edge computing, mobile systems, and enterprise environments.
What you will learn:
- How to analyze fundamental NPU architectural principles and specialized processing capabilities for neural network computations
- Understanding of the design of heterogeneous system integration strategies that optimize workload coordination across CPU-GPU-NPU environments
- How to implement comprehensive NPU deployment frameworks for diverse industrial applications and enterprise environments
- How to validate NPU performance through industry-specific profiling methodologies and integration testing protocols
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: 978-1-7281-7905-6