Prediction with data

Posted on by lechuck park

This image illustrates a comparison between two approaches for Prediction with Data.

Left Side: Traditional Approach (Setup First Configuration)

The traditional method consists of:

  • Condition: 3D environment and object locations
  • Rules: Complex physics laws
  • Input: 1+ cases
  • Output: 1+ prediction results

This approach relies on pre-established rules and physical laws to make predictions.

Right Side: Modern AI/Machine Learning Approach

The modern method follows these steps:

  1. Huge Data: Massive datasets represented in binary code
  2. Machine Learning: Pattern learning from data
  3. AI Model: Trained artificial intelligence model
  4. Real-Time High Resolution Data: High-quality data streaming in real-time
  5. Prediction Anomaly: Final predictions and anomaly detection

Key Differences

The most significant difference is highlighted by the question “Believe first ??” at the bottom. This represents a fundamental philosophical difference: the traditional approach starts by “believing” in predefined rules, while the AI approach learns patterns from data to make predictions.

Additionally, the AI approach features “Longtime Learning Verification,” indicating continuous model improvement through ongoing learning and validation processes.

The diagram effectively contrasts rule-based prediction systems with data-driven machine learning approaches, showing the evolution from deterministic, physics-based models to adaptive, learning-based AI systems.

With Claude

Server Room Workload

Posted on by lechuck park

This diagram illustrates a server room thermal management system workflow.

System Architecture

Server Internal Components:

  • AI Workload, GPU Workload, and Power Workload are connected to the CPU, generating heat

Temperature Monitoring Points:

  • Supply Temp: Cold air supplied from the cooling system
  • CoolZone Temp: Temperature in the cooling zone
  • Inlet Temp: Server inlet temperature
  • Outlet Temp: Server outlet temperature
  • Hot Zone Temp: Temperature in the heat exhaust zone
  • Return Temp : Hot air return to the cooling system

Cooling System:

  • The Cooling Workload on the left manages overall cooling
  • Closed-loop cooling system that circulates back via Return Temp

Temperature Delta Monitoring

The bottom flowchart shows how each workload affects temperature changes (ΔT):

  • Delta temperature sensors (Δ1, Δ2, Δ3) measure temperature differences across each section
  • This data enables analysis of each workload’s thermal impact and optimization of cooling efficiency

This system appears to be a data center thermal management solution designed to effectively handle high heat loads from AI and GPU-intensive workloads. The comprehensive temperature monitoring allows for precise control and optimization of the cooling infrastructure based on real-time workload demands.

With Claude

AI LLM Co-Evolutions

Posted on by lechuck park

This image illustrates the AI LLM Co-Evolution process, showing how Large Language Models develop through two complementary approaches.

The diagram centers around LLM with two main development pathways:

1. Model-Centric Development

  • More Diverse Conditions: Handling various data types and scenarios (represented by geometric shapes)
  • Analysis of Probabilities: Probabilistic approaches to model behavior (shown with dice icons)
  • Costs Efficiency: Economic optimization in model development (depicted with dollar sign and gear)

2. Data-Centric Development

  • Easier AI Analysis: Simplified analytical processes (represented by network diagrams)
  • Deterministic Data: Predictable and structured data patterns (shown with concentric circles)
  • Continuous Structural Adaptation: Ongoing improvements and adjustments (illustrated with checklist icon)

The diagram demonstrates that modern AI development requires both model-focused and data-focused approaches to work synergistically. Each pathway offers distinct advantages:

  • Model-centric focuses on architectural improvements, probabilistic reasoning, and computational efficiency
  • Data-centric emphasizes data quality, deterministic processes, and adaptive structures

This co-evolutionary framework suggests that the most effective LLM development occurs when both approaches are integrated, allowing for comprehensive advancement in AI system capabilities through their complementary strengths.

With Claude

AI Chips

Posted on by lechuck park

This image presents a comprehensive overview of the AI chip ecosystem, categorizing different approaches and technologies:

Major AI Chip Categories

GPU-Based Solutions:

  • Nvidia H100/B200 (AMD MI Series): Currently the most widely used GPUs for AI training and inference
  • General GPU architecture: Traditional general-purpose GPU architectures

Specialized AI Chips:

  • Cerebras AI (WSE): Wafer-Scale Engine where the entire wafer functions as one chip
  • Google TPU: Google’s Tensor Processing Unit
  • MS Azure Maia: Microsoft’s cloud-optimized AI chip
  • Amazon (Inferentia/Trainium): Amazon’s dedicated inference and training chips

Technical Features

Memory Technologies:

  • High-Bandwidth Memory (HBM): Advanced memory technology including HBM2E
  • Massive On-Chip SRAM: Large-capacity on-chip memory with external MemoryX
  • Ultra-Low Latency On-Chip Fabric (SwarmX): High-speed on-chip interconnect

Networking Technologies:

  • NvLink/NvSwitch: Nvidia’s high-speed interconnect with Infinity Fabric
  • Inter-Chip Interconnect (ICI): Ethernet-based connections including RoCE-like and UEC protocols
  • NeuroLink: Advanced chip-to-chip communication

Design Approaches:

  • Single Wafer-Scale Engine: Entire wafer as one chip with immense on-chip memory/bandwidth
  • Simplified Distributed Training: Wafer-scale design enabling simplified distributed training
  • ASIC for special AI function: Application-specific integrated circuits optimized for AI workloads
  • Optimization for Cloud Solutions with ASIC: Cloud-optimized ASIC implementations

This diagram effectively illustrates the evolution from general-purpose GPUs to specialized AI chips, showcasing how different companies are pursuing distinct technological approaches to meet the demanding requirements of AI workloads. The ecosystem demonstrates various strategies including memory optimization, interconnect technologies, and architectural innovations.

With Claude

Digital Twin with LLM

Posted on by lechuck park

This image demonstrates the revolutionary applicability of Digital Twin enhanced by LLM integration.

Three Core Components of Digital Twin

Digital Twin consists of three essential elements:

  1. Modeling – Creating digital replicas of physical objects
  2. Data – Real-time sensor data and operational information collection
  3. Simulation – Predictive analysis and scenario testing

Traditional Limitations and LLM’s Revolutionary Solution

Previous Challenges: Modeling results were expressed only through abstract concepts like “Visual Effect” and “Easy to view of complex,” making practical interpretation difficult.

LLM as a Game Changer:

  • Multimodal Interpretation: Transforms complex 3D models, data patterns, and simulation results into intuitive natural language explanations
  • Retrieval Interpretation: Instantly extracts key insights from vast datasets and converts them into human-understandable formats
  • Human Interpretation Resource Replacement: LLM provides expert-level analytical capabilities, enabling continuous 24/7 monitoring

Future Value of Digital Twin

With LLM integration, Digital Twin evolves from a simple visualization tool into an intelligent decision-making partner. This becomes the core driver for maximizing operational efficiency and continuous innovation, accelerating digital transformation across industries.

Ultimately, this diagram emphasizes that LLM is the key technology that unlocks the true potential of Digital Twin, demonstrating its necessity and serving as the foundation for sustained operational improvement and future development.

With Claude