Chiplet-Based_AI_Accelerators_Presentation.pptx

Education

1. Chiplet-Based AI Accelerators: The Future of High-Performance Computing Paper Presentation | ECE & Software Intersection

2. Introduction • AI accelerators are essential for deep learning and HPC. • Traditional monolithic chips face scalability & power limits. • Chiplets offer modularity, efficiency, and cost savings.

3. What Are Chiplet-Based AI Accelerators? • Chiplets are small, specialized processor units combined to form powerful AI accelerators. • Used in AI data centers, cloud computing, and edge AI. • Examples: AMD Instinct MI300, Intel Ponte Vecchio, NVIDIA Grace Hopper.

4. How Do They Work? • Chiplets use 2D, 2.5D, or 3D packaging for efficient processing. • High-speed interconnects (Infinity Fabric, Foveros, CoWoS) enable seamless data transfer. • Improves deep learning training & inference speeds.

5. Advantages of Chiplets in AI ✅ Higher Efficiency – Reduces power consumption. ✅ Scalability – Easier to add more processing units. ✅ Lower Cost – Simplifies semiconductor manufacturing. ✅ High-Speed AI Processing – Enables massive parallel computations.

6. Applications & Future Trends • AI Data Centers – Google TPUs, Microsoft Azure AI Chips. • AI in Edge Devices – IoT, autonomous vehicles, robotics. • Future: Quantum + Chiplet Integration for AI.

7. Conclusion • Chiplet-based AI accelerators are shaping the future of computing. • Offers high efficiency, modularity, and cost savings. • Key players like AMD, Intel, NVIDIA driving innovations. • Exciting research opportunities in AI & chip design!