Neural Network Quantization via Layer-wise Performance Feedback
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Solution Overview
Problem
Deep learning models require significant resources and operational complexity, making it challenging to achieve high performance in embedded systems with hardware restrictions, particularly in on-device AI applications.
Innovation Solution
An electronic apparatus and method for quantizing neural network models by acquiring and testing layers, transmitting quantized models, and receiving result data and profile information to determine if they meet performance conditions, adding suitable models to candidates for further optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models are applied to achieve high performance in AI tasks, then recognition accuracy and intelligence level are improved, but hardware resource requirements and operational complexity increase significantly
Solution Approach 1:
The patent applies parameter changes by transforming the precision parameter of neural network weights from high-precision floating-point format to low-precision integer format (e.g., 8-bit, 4-bit, or 2-bit quantization). This parameter transformation reduces the computational complexity and memory requirements while maintaining acceptable recognition accuracy, directly resolving the contradiction between accuracy and operational complexity
2Reliability
If deep learning models are applied to achieve high performance in AI tasks, then intelligence level is improved, but memory capacity requirements increase
Solution Approach 1:
The patent changes the precision parameter of model weights and activations from 32-bit floating-point to lower bit-depth integer representations. This parameter transformation directly reduces the memory capacity required to store model parameters and intermediate computations, enabling deployment of intelligent systems on devices with limited memory resources
3Device complexity
If quantization is applied to reduce resource requirements, then operational complexity is reduced, but model performance may deteriorate
Solution Approach 1:
The patent employs feedback mechanisms through calibration processes where the system executes the quantized model on test data, monitors performance metrics, and uses this feedback to optimize quantization parameters such as scale factors and zero-point values. This iterative feedback loop enables the system to achieve optimal balance between reduced operational complexity and maintained model performance
Solution Approach 2:
The patent applies preliminary action by performing quantization calibration and optimization before actual deployment. The system pre-computes quantization parameters, performs sensitivity analysis on different quantization schemes, and selects optimal configurations in advance, thereby minimizing performance loss while achieving the desired reduction in operational complexity
Data Source
AI summary
An electronic apparatus is provided. The electronic apparatus includes a memory and a processor, wherein the processor is configured to acquire a neural network model, test data for the neural network model, and information on required performance condition, quantize a layer among a plurality of layers comprised in the neural network model and acquire a first quantized neural network model, transmit the first quantized neural network model and the test data to a target apparatus, receive, from the target apparatus, result data acquired from the first quantized neural network model with the test data as an input, and profile information of the target apparatus related to the first quantized neural network model, and based on the result data and the profile information, based on the first quantized neural network model satisfying the required performance condition, add the first quantized neural network model to available quantized neural network model candidates.