Tag: AIComputing

Next AI Computing

Posted on by lechuck park

The Evolution of AI Computing

The provided images illustrate the architectural shift in AI computing from the traditional “Separation” model to a “Unified” brain-inspired model, focusing on overcoming energy inefficiency and data bottlenecks.

1. CURRENT: The Von Neumann Wall (Separation)

  • Status: The industry standard today.
  • Structure: Computation (CPU/GPU) and Memory (DRAM) are physically separate.
  • Problem: Constant data movement between components creates a “Von Neumann Wall” (bottleneck).
  • Efficiency: Extremely wasteful; 60-80% of energy is consumed just moving data, not processing it.

2. BRIDGE: Processing-In-Memory (PIM) (Proximity)

  • Status: Practical, near-term solution; nearly commercial-ready.
  • Structure: Small processing units are embedded inside the memory.
  • Benefit: Processes data locally to provide a 2-10x efficiency boost.
  • Primary Use: Ideal for accelerating Large Language Models (LLMs).

3. FUTURE: Neuromorphic Computing (Unity)

  • Status: Future-oriented paradigm shift.
  • Structure: Compute IS memory, mimicking the human brain’s architecture where memory elements perform calculations.
  • Benefit: Eliminates data travel entirely, promising a massive 1,000x+ energy improvement.
  • Requirement: Requires a complete overhaul of current software stacks.
  • Primary Use: Ultra-low power Edge devices and Robotics.

#AIComputing #NextGenAI #VonNeumannWall #PIM #ProcessingInMemory #NeuromorphicComputing #EnergyEfficiency #LLM #EdgeAI #Semiconductor #FutureTech #ComputerArchitecture

With Gemini

New For AI

Posted on by lechuck park

Analysis of “New For AI” Diagram

This image, titled “New For AI,” systematically organizes the essential components required for building AI systems.

Structure Overview

Top Section: Fundamental Technical Requirements for AI (Two Pillars)

Left Domain – Computing Axis (Turquoise)

  1. Massive Data
    • Processing vast amounts of data that form the foundation for AI training and operations
  2. Immense Computing
    • Powerful computational capacity to process data and run AI models

Right Domain – Infrastructure Axis (Light Blue)

3. Enormous Energy
Large-scale power supply to drive AI computing

  1. High-Density Cooling
    • Effective heat removal from high-performance computing operations

Central Link 🔗

Meaning of the Chain Link Icon:

  • For AI to achieve its performance, Computing (Data/Chips) and Infrastructure (Power/Cooling) don’t simply exist in parallel
  • They must be tightly integrated and optimized to work together
  • Symbolizes the interdependent relationship where strengthening only one side cannot unlock the full system’s potential

Bottom Section: Implementation Technologies (Stability & Optimization)

Learning & Inference/Reasoning (Learning and Inference Optimization)

Technologies to enhance AI model performance and efficiency:

  • Evals/Golden Set: Model evaluation and benchmarking
  • Safety Guardrails, RLHF-DPO: Safety assurance and human feedback-based learning
  • FlashAttention: Memory-efficient attention mechanism
  • Quant(INT8/FP8): Computational optimization through model quantization
  • Speculative/MTP Decoding: Inference speed enhancement techniques

Massive Parallel Computing (Large-Scale Parallel Computing)

Hardware and network technologies enabling massive computation:

  • GB200/GB300 NVL72: NVIDIA’s latest GPU systems
  • HBM: High Bandwidth Memory
  • InfiniBand, NVlink: Ultra-high-speed interconnect technologies
  • AI factory: AI-dedicated data centers
  • TPU, MI3xx, NPU, DPU: Various AI-specialized chips
  • PIM, CxL, UvLink: Memory-compute integration and next-gen interfaces
  • Silicon Photonics, UEC: Optical communication technologies

More Energy, Energy Efficiency (Energy Supply and Efficiency)

Technologies for stable and efficient power supply:

  • Smart Grid: Intelligent power grid
  • SMR: Small Modular Reactor (stable large-scale power source)
  • Renewable Energy: Renewable energy integration
  • ESS: Energy Storage System (power stabilization)
  • 800V HVDC: High-voltage direct current transmission (loss minimization)
  • Direct DC Supply: Direct DC supply (eliminating conversion losses)
  • Power Forecasting: AI-based power demand prediction and optimization

High Heat Exchange & PUE (Heat Exchange and Power Efficiency)

Securing cooling system efficiency and stability:

  • Liquid Cooling: Liquid cooling (higher efficiency than air cooling)
  • CDU: Coolant Distribution Unit
  • D2C: Direct-to-Chip cooling
  • Immersing: Immersion cooling (complete liquid immersion)
  • 100% Free Cooling: Utilizing external air (energy saving)
  • AI-Driven Cooling Optimization: AI-based cooling optimization
  • PUE Improvement: Power Usage Effectiveness (overall power efficiency metric)

Key Message

This diagram emphasizes that for successful AI implementation:

  1. Technical Foundation: Both Data/Chips (Computing) and Power/Cooling (Infrastructure) are necessary
  2. Tight Integration: These two axes are not separate but must be firmly connected like a chain and optimized simultaneously
  3. Implementation Technologies: Specific advanced technologies for stability and optimization in each domain must provide support

The central link particularly visualizes the interdependent relationship where “increasing computing power requires strengthening energy and cooling in tandem, and computing performance cannot be realized without infrastructure support.”

Summary

AI systems require two inseparable pillars: Computing (Data/Chips) and Infrastructure (Power/Cooling), which must be tightly integrated and optimized together like links in a chain. Each pillar is supported by advanced technologies spanning from AI model optimization (FlashAttention, Quantization) to next-gen hardware (GB200, TPU) and sustainable infrastructure (SMR, Liquid Cooling, AI-driven optimization). The key insight is that scaling AI performance demands simultaneous advancement across all layers—more computing power is meaningless without proportional energy supply and cooling capacity.

#AI #AIInfrastructure #AIComputing #DataCenter #AIChips #EnergyEfficiency #LiquidCooling #MachineLearning #AIOptimization #HighPerformanceComputing #HPC #GPUComputing #AIFactory #GreenAI #SustainableAI #AIHardware #DeepLearning #AIEnergy #DataCenterCooling #AITechnology #FutureOfAI #AIStack #MLOps #AIScale #ComputeInfrastructure

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