Optimizing Generative AI on Arm Processors for Edge and Cloud

Running generative AI models efficiently requires understanding the underlying hardware. As Arm processors power everything from mobile devices to massive cloud servers, learning how to optimize models for this architecture is a crucial skill for modern AI developers.\n\nThis text-based course guides you through the process of adapting and accelerating generative AI workloads on Arm-based systems. You will transition from understanding basic hardware concepts to implementing advanced optimization techniques that dramatically reduce latency and memory usage.\n\nWhat you'll learn:\n- Understand the foundational architecture of Arm processors, including CPU design and memory hierarchies\n- Apply SIMD vectorization techniques using Neon and Scalable Vector Extension (SVE) instructions to accelerate mathematical operations\n- Implement low-bit quantization strategies, such as INT4 and FP8, to compress large language models without sacrificing accuracy\n- Configure and utilize the optimized KleidiAI library to streamline key generative AI operations\n- Deploy optimized models across both constrained edge devices and scalable cloud environments\n- Analyze performance bottlenecks using modern profiling concepts and runtime tools\n\nYou will start with core hardware terminology and foundational concepts of CPU computation before exploring practical optimization strategies. Through step-by-step written explanations and clear code examples, you will learn to configure, optimize, and evaluate generative models for real-world deployment.\n\nThis course is designed for software developers, AI enthusiasts, and system engineers who are new to hardware-level optimization. No prior experience with hardware programming or assembly language is required.\n\nStart your journey into hardware-aware AI optimization today.