It presents ‘Multiple threads’, ‘Time-slicing’, and ‘Flattened load’ as its main features.
It shows a time-slicing method in the form of bar charts, where multiple threads (Thread 1 to 4) run one by one sequentially by dividing time.
The Power Load graph over time remains constant without large fluctuations, and this state is described as ‘Smooth & Predictable’.
AI Computing (GPU)
It presents ‘SIMT’, ‘Massive parallel’, and ‘Spiky load’ as its main features.
It depicts a massive parallel processing structure where thousands of cores execute the same instruction simultaneously (lock-step), represented by a grid of numerous blue squares.
The Power Load graph shows a ‘Spiky’ pattern, with power consumption sharply peaking at specific times because it performs operations at 100% at once.
Summary
A CPU processes multiple tasks sequentially by dividing time, resulting in stable and predictable power consumption. In contrast, a GPU utilizes thousands of cores to process massive calculations in parallel all at once, leading to a structural difference where the power load temporarily spikes.
The provided image is an infographic that visually compares the operational mechanisms of traditional computing and modern Artificial Intelligence (AI). The addition of the keywords “Deterministic” and “Probabilistic” at the bottom perfectly summarizes the core difference between these two paradigms.
1. The World of Deterministic Computing
This section explains the traditional computer mechanism, which consistently produces the same output based on predefined, rigid rules.
Step 1: The Foundation of Computing
Visuals: An intuitive ON/OFF power switch and an illuminated lightbulb.
Meaning: Computing begins with the fundamental Binary System, which distinguishes between two clear states: 0 (OFF) and 1 (ON).
Step 2: Classical Processing
Visuals: Logic gate symbols (AND, OR, NOT) interlocked with gears.
Meaning: It illustrates how conventional computers process binary inputs mechanically by applying predefined human rules and logical operations (Rule-based Processing).
2. The Paradigm Shift
Step 3: Questioning and Transition
Visuals: A brain integrated with electronic circuits, a computer, a robot icon, and a large question mark in the center.
Meaning: This represents a technological leap, asking the core question: “How does AI fundamentally differ from classical rule-based computing?”
3. The World of Probabilistic Computing
This section explains AI’s mechanism, which relies on data statistics and probabilities to self-learn and generate flexible outcomes.
Step 4: AI & LLMs (Large Language Models)
Visuals: A cloud containing clustered data nodes of various colors and statistical charts showing probabilities like 85% and 60%.
Meaning: Instead of making strict 0/1 distinctions, AI groups massive amounts of data into Clusters based on statistical Probabilities.
Step 5: AI Processing Mechanism
Visuals: A complex Artificial Neural Network structure combined with processing gears, leading to output files labeled “Generated” (images) and “Classified” (documents).
Meaning: Without relying on explicit human programming, AI autonomously learns weights and internal patterns (Self-Learning) from these probabilistic clusters to create new content or classify data.
📌 Summary
This infographic acts as a visual map showcasing the evolution of computing history from the era of “Deterministic Rules” to the era of “Probabilistic Self-Learning.”
It intuitively conveys the core difference: while early computers relied on clear 0/1 distinctions and explicit human-written code, modern AI (like LLMs) groups vast amounts of data by probability and autonomously learns internal patterns and weights to deliver flexible, creative, and highly advanced results.
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.
PUE Improvement: Power Usage Effectiveness (overall power efficiency metric)
Key Message
This diagram emphasizes that for successful AI implementation:
Technical Foundation: Both Data/Chips (Computing) and Power/Cooling (Infrastructure) are necessary
Tight Integration: These two axes are not separate but must be firmly connected like a chain and optimized simultaneously
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.
