Solving AI's Hardware Challenge for Optimal Deep Learning Potential

Timestamped Summary

• 🤖
  00:00

  OpenAI, Google, and others are pushing the boundaries of deep learning with increasingly larger models, but the Von Neumann architecture limits hardware capabilities.
• 🤔
  01:55

  Memory Wall is a limitation caused by the gap between AI models and GPUs, leading to expensive hardware and energy costs that could restrict AI benefits to the wealthy.
• 🤔
  04:54

  In the past 40 years, DRAM memories have become increasingly difficult to scale due to their low latency and cheap manufacturing.
• 🤖
  06:40

  Compute-in-Memory integrates processing elements into RAM, making it ideal for deep learning and edge computing.
• 🤔
  08:56

  Making logic circuits with DRAM or DRAM cells with logic processes both have significant drawbacks.
• 🤔
  11:32

  ReRAM is close to commercialization, but DRAM can also be used to implement logic functions with three rows.
• 🤔
  13:59

  AMD is using 3D V-Cache to add more memory cache to their processor chips, enabling hundreds of gigabytes of memory for AI ASICs.
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  15:45

  Developing new systems and hardware is necessary to unlock the full potential of deep learning.