Neural Data Compression Configuration via Distribution Inspection
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Solution Overview
Problem
Existing data compressors for deep learning systems struggle to achieve optimal compression of sparse data without increasing hardware area, latency, and complexity.
Innovation Solution
The technology involves inspecting the distribution of data to infer the data type and granularity, allowing for dynamic configuration of compressors and decompressors to achieve optimal compression without the need for multiple copies of compressors or complex entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple copies of compressors are used to handle different data types, then compression performance for various data types is improved, but hardware area and device complexity increase
Solution Approach 1:
The patent implements a universal compressor that can handle multiple data types (FP16, FP32, INT8) through a single configurable hardware unit. The compressor uses dynamic configuration where control logic selects appropriate compression parameters and algorithms based on the input data type, eliminating the need for separate dedicated compressors for each data type while maintaining optimal compression performance for all supported formats
Solution Approach 2:
The compressor employs dynamic configuration capabilities where compression parameters, algorithms, and operational modes are adjusted in real-time based on the detected data type. This dynamic adaptability allows a single hardware unit to optimize its behavior for different data formats (FP16, FP32, INT8) without requiring static dedicated hardware for each type, thus reducing overall hardware area while maintaining high compression efficiency
2Adaptability or versatility
If the same physical compressor is used multiple times with different configurations, then compression adaptability is improved, but latency and processing time increase
Solution Approach 1:
The system performs preliminary detection of the data type before compression begins. Control logic analyzes incoming data to identify its format (FP16, FP32, INT8) and pre-configures the compressor parameters and algorithms accordingly. This preliminary configuration step ensures that the compressor is optimally set up before actual compression starts, avoiding multiple sequential compression attempts and reducing overall latency
Solution Approach 2:
The compressor implements dynamic parameter adjustment based on real-time data type detection. When a new data stream arrives, the control logic automatically reconfigures the compressor's internal parameters, algorithms, and operational modes to match the detected data type. This dynamic adaptation occurs in a single pass rather than through multiple sequential compression attempts, maintaining low latency while achieving high adaptability across different data formats
3Productivity
If post-processing is used to compensate for sub-optimal compression configuration, then compression quality is improved, but device complexity and processing overhead increase
Solution Approach 1:
The system performs preliminary detection and configuration of compression parameters based on data type analysis before the actual compression process. The control logic identifies whether incoming data is FP16, FP32, or INT8 format and pre-configures the appropriate compression algorithm and parameters. This upfront configuration eliminates the need for complex post-processing adjustments, as the compression is optimized from the start based on the detected data characteristics
Solution Approach 2:
The system incorporates feedback mechanisms where the control logic continuously monitors the input data characteristics and adjusts compression parameters in real-time. Based on feedback from data type detection and compression performance monitoring, the system dynamically selects and switches between different compression algorithms (such as sparse compression for neural network data versus standard compression for other formats). This feedback-driven approach achieves optimal compression quality without requiring complex post-processing, as adjustments are made proactively during the compression process itself
Data Source
AI summary
Distribution of data in a neural network data set is used to determine an optimal compressor configuration for compressing the neural network data set and/or the underlying data type of the neural network data set. By using a generalizable optimization of examining the data prior to compressor invocation, the example non-limiting technology herein makes it possible to tune a compressor to better target the incoming data. For sparse data compression, this step may involve examining the distribution of data (e.g., in one example, zeros in the data). For other algorithms, it may involve other types of inspection. This changes the fundamental behavior of the compressor itself. By inspecting the distribution of data (e.g., zeros in the data), it also possible to very accurately predict the data width of the underlying data. This is useful because this data type is not always known a priori, and lossy compression algorithms useful for deep learning depend on knowing the true data type to achieve good compression rates.