Efficient AI is more than algorithms. This hands-on course shows you how to optimize GenAI workloads on real-world systems using techniques such as SIMD (SVE, Neon), low-bit quantization, and the optimized KleidiAI library. You will develop essential strategies and skills to build scalable, high-performance AI on edge and cloud-based platforms based on the most widespread processor architecture.
What you'll learn
You will learn how to optimize AI inference using Arm-specific techniques such as SIMD (SVE, Neon) and low-bit quantization. The course covers practical strate…
Syllabus 8
Module 1:Challenges Facing Cloud and Edge GenAI Inference
Understanding the limitations and constraints of AI inference in different environments.
Module 2:Generative AI Models
Exploring model architectures, training methodologies, and deployment considerations.
Module 3:ML Frameworks and Optimized Libraries
A deep dive into AI software stacks, including PyTorch, Llama.cpp, and Arm-specific optimizations.
Module 4: Optimization for CPU Inference
Techniques such as quantization, pruning, and leveraging SIMD instructions for faster AI performance.
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