AI Model Compression by Data Distribution Categorization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current storage solutions struggle to keep up with the increasing demands of artificial intelligence (AI) workloads due to limited data movement bandwidths and storage capacity, leading to high latency and inefficiencies.
Innovation Solution
The proposed solution involves categorizing AI model data based on its distribution analysis, selecting an appropriate compression algorithm, and storing the compressed data in a storage device. This method includes generating address boundary information and storing a mapping between the address boundary information and the compression algorithm to facilitate efficient decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is compressed using a single compression algorithm, then the compression process is simple, but the compression ratio is insufficient for diverse data distributions
Solution Approach 1:
The patent segments the compression process into multiple stages: first analyzing the data distribution to categorize it into different types (e.g., uniform, Gaussian, skewed), then selecting and applying different compression algorithms appropriate for each category. This segmentation allows the system to achieve high compression ratios for diverse data types while maintaining implementation simplicity through automated categorization.
Solution Approach 2:
The patent changes the parameter of compression algorithm selection based on data distribution characteristics. By analyzing histogram data and categorizing the distribution type, the system dynamically selects the most appropriate compression algorithm for each data category, thereby optimizing compression ratios without significantly increasing system complexity.
2Loss of substance
If multiple compression algorithms are used to achieve higher compression ratios, then the compression ratio improves, but the system complexity increases
Solution Approach 1:
The patent performs preliminary data distribution analysis and categorization before applying compression algorithms. By pre-analyzing the data characteristics and determining the appropriate compression algorithm in advance, the system avoids the complexity of real-time algorithm selection during compression, thereby achieving high compression ratios with manageable system complexity.
Solution Approach 2:
The patent introduces an intermediary component (data distribution analyzer/categorizer) that sits between the raw data and the compression algorithms. This intermediary analyzes the data characteristics and routes the data to the appropriate compression algorithm, thereby managing system complexity through modular design while achieving optimal compression ratios.
3Speed
If data is not compressed, then the data access is fast and simple, but the storage capacity and data movement bandwidth are insufficient
Solution Approach 1:
The patent applies partial compression by selectively compressing only the portions of data that benefit most from compression based on their distribution characteristics. By categorizing data and applying compression only where appropriate, the system achieves significant storage capacity improvements while minimizing the impact on data access speed, as not all data requires full compression processing.
Data Source
AI summary
Provided are systems, methods, and apparatuses for compression of artificial intelligence models. In one or more examples, the systems, devices, and methods include categorizing data based on an analysis of a distribution of the data, generating compressed data based on the data and on a compression algorithm that is selected based on the categorization, and storing the compressed data in a storage device. In one or more examples, the systems, devices, and methods include identifying an address associated with compressed data based on a request for the compressed data, determining a decompression algorithm based on the address, and decompressing the compressed data using the determined decompression algorithm.