AI Processors: Emerging Trends and Evolving Architectures
This course delivers a concise yet comprehensive technical exploration of the rapidly evolving world of AI processor architectures. It examines how modern computational systems are being reshaped by architectural innovation, hardware acceleration strategies, and energy‑aware design methodologies. Participants develop a structured understanding of the historical evolution of processor architectures, the emergence of specialized compute units, and the performance‑driven considerations guiding next‑generation AI hardware. Through an analytical, forward‑looking framework, learners gain the ability to evaluate technological developments, interpret efficiency trade‑offs, and anticipate future architectural trajectories informed by IEEE standardization and regulatory frameworks. The course equips attendees with the technical insight needed to assess current processor technologies and contribute strategically to future AI hardware design.
What you will learn:
- Understanding of emerging paradigms and long-term architectural trajectories
- Integrating specialized compute units and forecasting future design requirements
- Evaluating acceleration, parallelism, and energy-performance dynamics
- Assessing architectural implications across edge, cloud, and heterogeneous environments
- Mapping AI workloads to evolving architectures and acceleration technologies
- Working with NPUs, TPUs, GPUs, and emerging co-processor designs
- Aligning model execution strategies with next-generation hardware capabilities
- Gaining forward-looking insight into architectural shifts and regulatory considerations
This course is part of the following course program:
AI Processor Architecture, Design Principles, and Performance
Courses included in this program:
Who Should Attend: AI Hardware Architects advancing understanding of emerging paradigms and long‑term architectural trajectories, Chip Designers and Microarchitecture Engineers analyzing architectural evolution, acceleration strategies, and efficiency trade‑offs, Processor and SoC Architects integrating specialized compute units and forecasting future design requirements, Performance Optimization Engineers evaluating acceleration, parallelism, and energy‑performance dynamics, Systems Engineers assessing architectural implications across edge, cloud, and heterogeneous environments, Power and Thermal Engineers focusing on sustainable, energy‑aware processor design, ML Hardware Specialists mapping AI workloads to evolving architectures and acceleration technologies, AI Accelerator Engineers working with NPUs, TPUs, GPUs, and emerging co‑processor designs, Hardware‑Software Co‑Design Engineers aligning model execution strategies with next‑generation hardware capabilities, AI Hardware Product Managers gaining forward‑looking insight into architectural shifts and regulatory considerations.
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: 978-1-7281-7906-3