Codebook Quantization Scale Search for Neural Network Compression
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Solution Overview
Problem
Current methods for finding optimal scale parameters and assignments in scalar quantization for neural network compression are computationally inefficient and cannot guarantee practical runtimes, leading to suboptimal mean-squared error (MSE) minimization.
Innovation Solution
The implementation of a scalable codebook-based quantization method that iteratively optimizes quantization scale parameters and assignments using intermediate points between codebook entries, allowing for the identification of globally-optimal quantization scale parameters within practical runtimes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the alternating optimization (ALTOPT) method is used to find optimal scale parameters, then the mean-squared error minimization is attempted, but the computational cost becomes excessively high and convergence to globally-optimal parameters cannot be guaranteed
Solution Approach 1:
The patent segments the continuous optimization problem into discrete steps by dividing the scale parameter search space into intervals defined by intermediate points. Instead of continuously iterating through all possible scale parameters, the method evaluates only specific intermediate points between codebook entries, transforming the continuous optimization into a discrete search that is computationally tractable while still achieving globally-optimal solutions.
Solution Approach 2:
The patent performs preliminary computation by pre-calculating intermediate points between codebook entries before the actual optimization process. These intermediate points serve as predetermined candidate scale parameters, eliminating the need for expensive iterative optimization during deployment. This preliminary preparation enables fast, guaranteed convergence without sacrificing accuracy.
2Measurement precision
If the grid-search method is used with smaller spacing between scale parameters, then higher overall accuracy is achieved, but the runtime becomes impractical
Solution Approach 1:
The patent extracts only the essential candidate scale parameters from the full grid search space by identifying and utilizing intermediate points between codebook entries. Instead of evaluating every possible scale parameter in a fine-grained grid, the method extracts and evaluates only those intermediate points that can potentially yield optimal solutions, dramatically reducing the search space while maintaining accuracy.
Solution Approach 2:
The patent changes the parameter representation by shifting from a continuous scale parameter to discrete intermediate points derived from codebook entry differences. This parameter transformation allows the method to achieve the same quantization accuracy as fine grid-search but with dramatically fewer evaluation points, resolving the trade-off between accuracy and runtime.
Data Source
AI summary
Apparatuses, systems, and techniques for calculating data compression parameters using codebook entry values. In at least one embodiment, one or more circuits is to calculate one or more data compression parameters based, at least in part, on at least on one or more values of the data to be compressed in relation to at least two codebook entry values.