Quantized Neural Network Error Compensation With Low-Rank Matrices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural networks require significant memory and computing resources due to large parameter sizes, leading to memory-bound execution and decreased accuracy when quantized, necessitating error compensation techniques to preserve performance.
Innovation Solution
Implement error compensation techniques using compensation matrices to minimize quantization error in quantized neural networks, applying low-rank approximations and singular value decomposition to determine optimal matrices for decompression during inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantization is applied to reduce memory usage, then memory consumption decreases, but accuracy deteriorates due to quantization errors
Solution Approach 1:
The patent introduces compensation matrices as an intermediary component between the quantized weights and the computation process. These compensation matrices capture and correct the quantization errors, allowing the system to maintain accuracy while using quantized weights for memory efficiency. The compensation matrices act as a mediator that bridges the gap between the compressed quantized representation and the original precision requirements.
Solution Approach 2:
The patent changes the parameters of the weight representation by introducing compensation terms that adjust the quantized weights. By modifying the weight parameters from simple quantized values to quantized values plus compensation matrix contributions, the system maintains computational accuracy while benefiting from the memory savings of quantization. This parameter transformation allows the model to operate in a hybrid precision regime.
2Measurement precision
If full precision weights are used to maintain accuracy, then accuracy is preserved, but memory consumption increases significantly
Solution Approach 1:
The patent segments the weight representation into two distinct components: quantized weights for memory-efficient storage and compensation matrices for accuracy preservation. Instead of storing full precision weights throughout, the system divides the weight parameter space into a compressed base representation and correction terms. This segmentation allows different parts of the weight system to serve different functions - storage efficiency and computational accuracy.
Solution Approach 2:
The patent extracts the quantization error component separately from the main weight computation. By identifying and isolating the error introduced by quantization, the system can store it in a compact compensation matrix form and apply it selectively during computation. This extraction allows the majority of weights to be stored in low-precision format while only the essential correction information is maintained in higher precision.
3Use of energy by moving object
If quantization is applied to improve memory efficiency, then memory bandwidth requirements decrease, but computational complexity increases due to error compensation
Solution Approach 1:
The patent applies partial compensation by using low-rank approximations of the full compensation matrices. Instead of applying complete error correction for all weight parameters, the system uses a reduced-rank representation that captures the most significant error components. This partial action approach provides sufficient accuracy improvement while avoiding the full computational overhead of complete compensation, achieving a balance between complexity and performance.
Data Source
AI summary
Apparatuses, systems, and techniques to compensate for quantization error for one or more quantized neural networks are described. In at least one embodiment, one or more compensation matrices determined based on one or more activations of one or more quantized neural networks are obtained and used to decompress the one or more quantized neural networks.