AI Processor Architecture, Design Principles, and Performance
This course program, developed by IEEE Educational Activities with the support from the IEEE Computer Society, provides a comprehensive exploration of the architectures, design principles, and performance foundations that define modern AI processors. Positioned at the intersection of computational design, electrical engineering, and machine learning, the program equips participants with a structured understanding of how AI processors are built, optimized, and deployed across diverse computing environments.
What You Will Learn:
- Seven key focus areas that define the architectural, design, and performance foundations of modern AI processors
- Fundamental AI processor architectures
- Design principles for AI hardware
- Performance analysis and optimization
- Advanced AI chip architectures
- Neural processing unit foundations
- Architectural evolution and technology trends
- Deployment across computing environments
Prerequisites:
Participants should have:
- A basic understanding of cybersecurity principles, including threats, vulnerabilities, risk, confidentiality, integrity, availability, authentication, and access control.
- Familiarity with enterprise IT and network environments, including TCP/IP, firewalls, cloud services, identity management, and common security controls.
- Introductory knowledge of cybersecurity risk management, governance, compliance, and incident response.
- General awareness of critical infrastructure environments, such as healthcare, transportation, energy, manufacturing, or other cyber-physical systems.
- Basic familiarity with Operational Technology (OT), Industrial Control Systems (ICS), or Supervisory Control and Data Acquisition (SCADA) is beneficial but not required.
- Introductory exposure to cybersecurity standards and regulations such as NIST Cybersecurity Framework, ISO/IEC 27001, HIPAA, IEC 62443, and NERC CIP is helpful.
- General understanding of security operations, cyber threat intelligence, supply chain risk, and business continuity is recommended.
- Basic awareness of emerging technologies such as artificial intelligence, machine learning, blockchain, and cryptography is useful, but advanced expertise is not required.
Recommended background: An undergraduate-level course, professional training, or relevant work experience in cybersecurity, information technology, computer science, engineering, risk management, compliance, industrial operations, or a related field. The program is suitable for both technical professionals and senior decision-makers responsible for critical infrastructure security.
Courses included in this program:
Who Should Attend: AI Hardware Architects, Chip Designers, Data Center Hardware Engineers, Edge Computing Engineers, Electrical and Computer Engineers, Embedded Systems Developers, Innovators exploring next‑generation, AI processor ecosystems, Microarchitecture Engineers, Machine Learning Hardware Specialists, Performance Optimization Engineers, Product Managers for AI Hardware Solutions, Researchers in AI Processor Design, Systems Engineers.
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: 978-1-7281-7908-7