Neural Network Weight Compression for GPU Memory Bottlenecks
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Solution Overview
Problem
The growth in the number of parameters in neural networks exceeds the capacity of hardware, leading to memory bottlenecks and inefficiencies in training and inference, particularly in distributed settings.
Innovation Solution
A method for dynamically compressing and decompressing neural network weights during training and inference, utilizing lossless compression for exponent bits and lossy compression for mantissa bits, allowing efficient memory usage without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of parameters in neural networks is increased to improve performance, then model accuracy and capability are improved, but memory usage and hardware requirements worsen
Solution Approach 1:
The patent segments the weight representation into two distinct parts: exponent bits and mantissa bits. This segmentation allows differential compression strategies to be applied to each part, enabling memory reduction while preserving critical precision information. The exponents are compressed losslessly while mantissas use lossy compression, achieving a balance between memory efficiency and model performance.
Solution Approach 2:
The patent changes the storage format of weight parameters from standard floating-point representation to a compressed format where exponents and mantissas are stored separately and compressed differently. This parameter change enables the system to achieve up to 75% memory savings while maintaining the numerical range and precision required for neural network operations.
2Quantity of substance
If compression is applied to neural network weights to reduce memory usage, then memory efficiency is improved, but computational accuracy worsens
Solution Approach 1:
The patent applies different compression qualities to different parts of the weight representation. Exponent bits are compressed losslessly to preserve the numerical range and scale information, while mantissa bits are compressed with controlled loss to reduce memory usage. This local quality differentiation ensures that critical precision information is retained while achieving significant memory savings.
Solution Approach 2:
The patent changes the storage precision parameters by storing exponents and mantissas separately with different compression levels. This allows the system to maintain the dynamic range (via lossless exponent compression) while reducing precision requirements (via lossy mantissa compression), achieving a optimal balance between memory efficiency and computational accuracy.
3Quantity of substance
If distributed training is used to overcome hardware memory limitations, then model size capacity is improved, but training efficiency worsens due to communication overhead
Solution Approach 1:
The patent segments the weight data structure to enable more efficient distributed storage and communication. By separating exponents and mantissas and applying different compression strategies, the system reduces the total data volume that needs to be communicated across distributed devices, thereby improving training efficiency while maintaining the ability to handle large model sizes.
Data Source
AI summary
A method for training or performing inference using a neural network involves performing per-layer decompression and compression of neural network weights. More particularly, compressed weights are retrieved for a particular layer of the neural network. The weights correspond to neurons in the layer. The compressed weights are decompressed, and input data for that layer is subsequently processed using the decompressed weights. This dynamic decompression and recompression of weights allows memory, and in particular random access memory of graphical processing units, to be efficiently used.