Objectives
After completing this course, the learner will be able to:
■ Explain AI cloud demand and benefits
■ Compare AI cloud vs. IT cloud
■ Identify types of AI hardware
■ Analyze CPU architectures and limits
■ Evaluate CPU advancements, FPGAs, and ASICs.
■ Describe GPU functions and advantages
■ Implement private cloud for AI training
Outline
1. Demand for AI Cloud
1.1 Growing need for AI infrastructure
1.2 Scalability and flexibility
1.3 Cost efficiency
1.4 Compare AI cloud vs IT cloud infrastructure
2. Overview of AI Hardware
2.1 Central Processing Units (CPUs)
2.2 Graphics Processing Units (GPUs)
2.3 Data Processing Units (DPUs)
2.4 Neural Processing Units (NPUs)
2.5 Tensor Processing Units (TPUs)
3. Deep Dive into CPU Architectures and Augmenting Hardware
3.1 Traditional CPU Architectures
3.2 Limitations of CPUs for AI
3.3 Advancements in CPU Design
3.4 Field-Programmable Gate Arrays (FPGAs)
3.5 Application-Specific Integrated Circuits (ASICs)
4. Deep Dive into GPUs
4.1 How GPUs Work
4.2 GPUs vs. CPUs for AI
4.3 Optimizing AI with GPUs
5. Private Cloud for AI
5.1 Private Cloud for Training
5.2 Private Cloud for Inference
5.3 Case Studies
6. Summary
