Neural Network Quantization for Low-Bit Convolution Accuracy
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Solution Overview
Problem
Neural networks face challenges in efficient data processing and memory usage due to their computational intensity, particularly in embedded systems like smartphones, where they require significant resources for accurate pattern recognition and generation tasks.
Innovation Solution
The method involves quantizing activation maps and weights using representation bit numbers and activation/weight quantization parameters, allowing for reduced bit precision in convolution operations, thereby optimizing data processing rates and memory usage without performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks use high precision floating-point calculations, then accuracy is maintained, but computational complexity and power consumption increase significantly
Solution Approach 1:
The patent changes the numerical representation parameter from high-precision floating-point format to low-precision quantized format (e.g., 8-bit integers). This parameter change reduces computational complexity and power consumption while maintaining acceptable accuracy through optimized quantization schemes that preserve critical information.
Solution Approach 2:
The patent replaces complex floating-point arithmetic operations with simpler integer arithmetic operations. This substitution reduces the computational burden on processors, enabling neural networks to run efficiently on resource-constrained devices like mobile phones and embedded systems.
2Productivity
If neural networks use low precision quantization, then computational efficiency improves, but accuracy deteriorates
Solution Approach 1:
The patent optimizes quantization parameters (such as scale factors and zero-points) to find the optimal balance between precision and efficiency. By carefully tuning these parameters, the system achieves high processing speed with minimal accuracy loss.
Solution Approach 2:
The patent applies different quantization precision levels to different parts of the neural network based on their importance. Critical layers maintain higher precision while less critical layers use lower precision, achieving overall efficiency without significant accuracy degradation.
3Reliability
If neural networks store full precision weights and activations, then model fidelity is preserved, but memory consumption increases
Solution Approach 1:
The patent changes the storage precision parameter from 32-bit floating-point to 8-bit or 16-bit quantized formats. This reduces memory usage by 4-16 times while maintaining model fidelity through carefully designed quantization schemes that preserve the essential characteristics of weights and activations.
Solution Approach 2:
The patent uses quantized copies of weights and activations instead of full-precision originals. These quantized copies occupy significantly less memory space while retaining sufficient information for accurate inference, enabling deployment on devices with limited memory resources.
4Measurement precision
If neural networks perform convolution operations with high bit precision, then computational accuracy is maintained, but processing speed decreases
Solution Approach 1:
The patent replaces complex floating-point multiplication and accumulation operations in convolution with simpler integer arithmetic operations. This substitution dramatically increases processing speed while maintaining sufficient accuracy through optimized quantization that preserves the statistical properties of the data.
Solution Approach 2:
The patent changes the numerical precision parameter used in convolution operations from high-precision floating-point to low-precision quantized format. This parameter change enables faster computation on standard hardware accelerators while maintaining acceptable accuracy through careful optimization of the quantization process.
Data Source
AI summary
Disclosed is a processor-implemented data processing method in a neural network. A data processing apparatus includes at least one processor, and at least one memory configured to store instructions to be executed by the processor and a neural network, wherein the processor is configured to, based on the instructions, input an input activation map into a current layer included in the neural network, output an output activation map by performing a convolution operation between the input activation map and a weight quantized with a first representation bit number of the current layer, and output a quantized activation map by quantizing the output activation map with a second representation bit number based on an activation quantization parameter.