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양자화란 무엇인가요

암호화폐 맥락에서 양자화는 연속적인 값 범위를 이산 집합에 매핑하는 것을 포함하며, 가격, 수수료 및 기타 숫자 데이터를 나타내는 데 자주 사용됩니다.

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English Deutsch Español Français 日本語 한국어 Polski Português Русский Türkçe Українська

양자화는 암호화폐 및 블록체인 기술을 포함한 디지털 시스템의 기본 개념입니다. 연속적인 값 범위를 이산 값 집합으로 변환하는 과정입니다. 이는 디지털 형식으로 가격이나 금액과 같은 실제 수량을 나타내는 데 필수적입니다.

암호화폐 분야에서 양자화는 가격 책정에 자주 적용됩니다. 예를 들어, 암호화폐 가격은 무한히 정확한 값을 허용하는 대신 0.01달러 또는 0.0001달러와 같은 특정 값의 증분으로만 표시될 수 있습니다. 이는 계산을 단순화하고 저장하거나 전송해야 하는 데이터의 양을 줄이기 위해 수행됩니다.

양자화는 또한 거래 수수료에도 영향을 미칩니다. 수수료는 분수 값을 허용하는 대신 이산 단위로 계산될 수 있습니다. 이렇게 하면 수수료 계산이 단순화되고 네트워크가 거래를 효율적으로 처리할 수 있습니다. 양자화가 없으면 값의 정밀도로 인해 복잡한 계산과 저장 공간이 증가하여 시스템의 효율성이 떨어질 수 있습니다.

양자화는 블록 크기의 표현 또는 기타 숫자 매개변수 처리와 같은 다른 영역에서도 볼 수 있습니다. 사용되는 특정 값과 증분은 특정 암호화폐 또는 블록체인 시스템의 설계에 따라 다릅니다.

        graph LR
  Center["양자화란 무엇인가요"]:::main
  Pre_mathematics["mathematics"]:::pre --> Center
  click Pre_mathematics "/terms/mathematics"
  Rel_non_fungible_tokens_nfts["non-fungible-tokens-nfts"]:::related -.-> Center
  click Rel_non_fungible_tokens_nfts "/terms/non-fungible-tokens-nfts"
  Rel_points_system["points-system"]:::related -.-> Center
  click Rel_points_system "/terms/points-system"
  Rel_tokenized_securities["tokenized-securities"]:::related -.-> Center
  click Rel_tokenized_securities "/terms/tokenized-securities"
  classDef main fill:#7c3aed,stroke:#8b5cf6,stroke-width:2px,color:white,font-weight:bold,rx:5,ry:5;
  classDef pre fill:#0f172a,stroke:#3b82f6,color:#94a3b8,rx:5,ry:5;
  classDef child fill:#0f172a,stroke:#10b981,color:#94a3b8,rx:5,ry:5;
  classDef related fill:#0f172a,stroke:#8b5cf6,stroke-dasharray: 5 5,color:#94a3b8,rx:5,ry:5;
  linkStyle default stroke:#4b5563,stroke-width:2px;
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🧒 5살도 이해할 수 있게 설명

Quantization is like rounding numbers on a calculator. Instead of writing down a super long number like 3.1415926535, you might just write 3.14 to make it shorter and easier to handle, even if you lose a tiny [bit](/ko/terms/bit) of exactness.

🤓 Expert Deep Dive

In digital signal processing and numerical computation, quantization is the approximation of a continuous or high-resolution set of values to a lower-resolution, discrete set. For cryptocurrencies, this manifests in several critical areas:

  1. Price Representation: Many exchanges and protocols quantize asset prices to a fixed number of decimal places (e.g., 8 for Bitcoin satoshis, or specific precision for stablecoins or synthetic assets). This prevents floating-point precision issues and standardizes price feeds. For instance, a price might be represented as an integer number of the smallest divisible unit (e.g., satoshis per BTC), effectively price_in_smallest_unit = round(actual_price (10^precision)). This reduces storage overhead and simplifies arithmetic operations.
  1. Transaction Fee Calculation: Fees are often calculated based on discrete units like gas (in Ethereum) or satoshis per byte. The underlying calculation might involve continuous metrics, but the final fee is rounded to a practical, discrete amount. For example, fee = ceil(base_fee_rate data_size_in_bytes). This ensures fees are always a whole number of the native currency's smallest unit, preventing minuscule, unmanageable fee values.
  1. Block Size and Gas Limits: Parameters like block size limits or gas limits per block are discrete integers. While the underlying complexity of transactions might vary continuously, the system enforces these hard, quantized limits to maintain network stability and predictability. A block's data size is thus sum(transaction_sizes), which must be <= MAX_BLOCK_SIZE.
  1. Tokenomics and Precision: The smallest divisible unit of a token (e.g., a wei for Ether, a satoshi for Bitcoin) is itself a form of quantization. All token amounts are represented as integers of these smallest units, e.g., 1 Ether = 10^18 wei. This avoids floating-point arithmetic and ensures precision at the base level.

Architecturally, this often involves using fixed-point arithmetic or integer representations to represent scaled values, avoiding the complexities and potential inaccuracies of floating-point numbers, especially in distributed and consensus-critical environments where deterministic outcomes are paramount.

🔗 관련 용어

선행 지식:

📚 출처

1. Quantization - Ethereum.org Glossary
2. Quantization - Gemini Cryptopedia
3. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
4. A Survey of Quantization Methods for Deep Neural Networks
5. Quantization - PyTorch Documentation
6. LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
7. QLoRA: Efficient Finetuning of Quantized LLMs
8. GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
9. 4-bit NormalFloat (NF4) Quantization
10. SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
11. LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
12. LLM.q: An Efficient Quantization Framework for Large Language Models
13. A Survey of Quantization Methods for Efficient Deep Learning
14. GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
15. Post-training quantization
16. LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
17. Quantizing deep convolutional networks for efficient integer-only inference
18. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
19. SmoothQuant: Accurate Quantization of Large Language Models
20. SpQR: Sparse-Quantized Representation for Near-Lossless LLM Compression
21. GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers