Genetic Algorithm Calibration for Neural Network Quantization Scale Factors
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Solution Overview
Problem
The calibration of scale factors for quantization in neural networks is challenging due to the mismatch in precision between calibration hardware and deployment hardware, leading to inaccurate results and increased processing time.
Innovation Solution
A genetic algorithm is employed to calibrate scale factors by generating an initial population of chromosomes, each representing a set of scale factors for layers of a machine learning model. The algorithm evaluates each chromosome by quantizing weights and datasets, processing them through an accelerator circuit, and gauging accuracy. The population is evolved until termination criteria are met, selecting the chromosome with the highest accuracy for configuring the accelerator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If floating point values are quantized to integer values using a scale factor, then hardware requirements are reduced and runtime performance is improved, but accuracy of the results is significantly affected
Solution Approach 1:
The patent changes the parameters being optimized from individual weight quantization to scale factor quantization. By treating scale factors as chromosomal genes and using genetic algorithms to evolve optimal scale factor combinations, the system maintains accuracy while achieving hardware efficiency. The scale factors are adjusted iteratively through selection, crossover, and mutation operations to find the optimal configuration.
Solution Approach 2:
The patent implements feedback by evaluating the accuracy of quantized results using a representative dataset and using this feedback to guide the genetic algorithm's evolution. The accuracy metric serves as the fitness function that determines which chromosomes (scale factor configurations) are selected for the next generation, creating a closed-loop optimization process that continuously improves accuracy while maintaining hardware benefits.
2Measurement precision
If scale factors are calibrated using genetic algorithms with population evolution, then accuracy is improved, but processing time is increased
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the optimal scale factors in a lookup table during an offline calibration phase. This pre-computed information is then reused during runtime without requiring repeated genetic algorithm executions, thus achieving high accuracy without incurring the computational time cost during actual inference operations.
Solution Approach 2:
The patent segments the calibration process into distinct phases: offline calibration using genetic algorithms to build a lookup table, and online inference using pre-computed scale factors. This segmentation allows the computationally intensive accuracy-optimizing process to occur separately from time-critical inference operations, resolving the time-accuracy tradeoff.
3Measurement precision
If the population of chromosomes is evolved through multiple generations, then the level of accuracy is improved, but the number of processing iterations increases
Solution Approach 1:
The patent applies partial action by performing a limited number of genetic algorithm generations during offline calibration rather than exhaustive evolution. The calibration process stops when a satisfactory accuracy threshold is reached or a maximum generation limit is hit, accepting a partial optimization that balances accuracy improvement with computational resource consumption.
Data Source
AI summary
Approaches for determining quantization scale factors include generating a population of chromosomes. Each chromosome has multiple genes, and each gene specifies a scale factor associated with a layer of a machine learning model. The population of chromosomes are evaluated, and the evaluating includes, for each chromosome in the population, quantizing floating point weights and floating point values of a representative dataset using the scale factors of the chromosome to produce quantized weights and a quantized dataset in the memory arrangement, initiating processing of the quantized dataset using the quantized weights according to the machine learning model, and gauging a level of accuracy of results produced by the processing of the quantized dataset. Satisfaction of termination criteria is determined based the levels of accuracy associated with the chromosomes in the population. The population of chromosomes is evolved and the evaluating repeated in response to the termination criteria not being satisfied.