CPU vs GPU – the Nvidia legendary story

Always good to know the basics of Computer Science : https://open.spotify.com/episode/0ax1sBKJ1ombOYGwWl0hEq

–> Great podcast to better understand what’s currently happening in the VR, AR, 3D and AI world!

1. Background on von Neumann Architecture

  • Core Concept: In the von Neumann architecture, programs are stored in memory alongside data. The processor fetches instructions and data from memory to execute programs, enabling computers to perform a wide range of tasks.
  • Memory Function:
    • Stores both data and instructions.
    • Allows for multiple programs, enabling multitasking.
  • Processor Function:
    • Executes programs by reading simple lines of code (following the Instruction Set Architecture – ISA, akin to bytecode).
    • Executes instructions sequentially, one operation at a time.

2. CPU Operations and Limitations

  • Basic Operations:
    • Loads data from memory into registers.
    • Fetches data, places it in the CPU register, and performs math operations.
  • Sequential Execution:
    • Processes one operation and one cycle at a time.
    • GHz measures the number of cycles per second.
  • Bottlenecks:
    • Steps must happen sequentially, limiting speed and efficiency.
    • Adding more memory doesn’t enhance speed indefinitely due to physical limitations.
    • Physics limits further speed increases, creating an upper boundary for CPU-based computation.

3. New Directions for Overcoming CPU Limitations

  • Parallel Processing as a Solution:
    • Transitioning to architectures that allow parallel execution of programs unlocks greater potential for processing power.
    • On-chip memory becomes crucial for handling the increased data demands of parallel computing.
    • Memory scaling is pivotal in AI, where memory bottlenecks affect performance significantly.

4. Technological Advances and AI Hardware Requirements

  • Memory Proximity to Processor:
    • Fast data transfer and massive memory capacity close to the CPU are essential for AI and 3D processing tasks.
    • Improved memory packaging (e.g., high bandwidth memory close to the CPU) enables faster parallel processing, critical for AI workloads.
  • EUV Lithography (Reference):
    • Extreme Ultraviolet (EUV) lithography discussed in other episodes; it highlights challenges in physics and wavelength that impact modern chip manufacturing.

5. Emerging Concepts and Industry Shifts

  • Rethinking Computer Architecture:
    • There’s exploration into “computing in memory,” where memory and processing capabilities are combined or situated much closer, enabling a fundamental redefinition of what a computer is.
    • Companies like TSMC (Taiwan Semiconductor Manufacturing Company) are at the forefront, investing in these evolving architectures.
  • Future Directions:
    • Redefining the concept of a computer by innovating with memory and processing configurations.
    • NVIDIA’s role in pushing these architectures forward, especially in AI, shows its influence in setting new industry standards.