Delivering Unprecedented Scale for AI Infrastructure and Cloud Connectivity

Overview

Discover how purpose-built connectivity solutions like PCIe, CXL, and Ethernet overcome performance bottlenecks and scale memory bandwidth, processing, and rack connectivity for AI infrastructure.

Syllabus

• Introduction to AI Infrastructure and Challenges
Overview of AI models and data requirements
Common performance bottlenecks in AI systems
• Purpose-Built Connectivity Solutions
Introduction to PCIe (Peripheral Component Interconnect Express)
Understanding CXL (Compute Express Link)
Role of Ethernet in AI infrastructure
• PCIe for AI
PCIe standards and evolution
Enhancing data transfer rates and connectivity
Use cases and implementations in AI
• CXL: Enabling Memory and Device Cohesion
Overview of CXL architecture and protocols
Benefits of CXL for AI processing
Real-world applications of CXL in AI systems
• Ethernet in AI Infrastructure
Ethernet standards relevant to AI
Scaling network bandwidth for distributed AI workloads
Case studies of Ethernet in AI connectivity
• Scaling Memory Bandwidth for AI
Memory hierarchy in AI systems
Challenges of memory scalability and latency
Solutions for overcoming memory bottlenecks
• Processing Performance Optimization
Optimizing computations across GPUs and CPUs
Data locality and bandwidth considerations
Techniques for processing efficiency in large-scale deployments
• Rack Connectivity for AI
Importance of high-performance rack interconnects
Integrating PCIe, CXL, and Ethernet at the rack level
Rack design considerations for AI workloads
• Case Studies and Industry Applications
Case study 1: Enhancing AI training with advanced connectivity
Case study 2: Scalability in AI inference applications
Future trends and developments in AI infrastructure
• Conclusion and Future Directions
Summary of key concepts
Emerging technologies and their impact on AI infrastructure scalability
Resources for continued learning and advancement in AI connectivity systems

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Subjects

Programming