Quantization Range Estimation for Neural Network Training
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Solution Overview
Problem
Current methods for training deep neural networks are inefficient on resource-constrained devices due to high computational costs and memory usage, and existing quantization techniques during back-propagation introduce noise that affects model accuracy.
Innovation Solution
The method employs hindsight-based quantization range estimation using historical tensor statistics to efficiently quantize gradients, activations, and weights during both forward and backward passes, allowing for reduced memory overhead and power consumption while maintaining accuracy by pre-computing quantization parameters based on prior iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-precision floating-point formats are used for training, then model accuracy is maintained, but computational cost and memory usage increase significantly
Solution Approach 1:
The patent changes the precision parameter of tensor data from full-precision floating-point to lower-bit quantized formats during training. By dynamically adjusting the bitwidth parameter based on tensor importance and available resources, the system reduces computational cost while maintaining model accuracy within acceptable ranges.
Solution Approach 2:
The patent implements dynamic quantization where the precision level of different tensors is adjusted during the training process based on their sensitivity to quantization. Important tensors maintain higher precision while less critical ones use lower precision, creating a dynamic adaptation strategy that balances accuracy and efficiency.
2Measurement precision
If network size is increased to improve accuracy, then model performance improves, but memory usage and computational complexity increase
Solution Approach 1:
The patent applies quantization parameter changes to reduce the memory footprint of large networks. By representing weights and activations in lower bitwidth formats, the effective model size is reduced while maintaining computational accuracy, enabling deployment on resource-constrained devices.
3Productivity
If quantization is applied during back-propagation, then computational efficiency improves, but noise is introduced that affects model accuracy
Solution Approach 1:
The patent applies different quantization strategies to different parts of the training process. Critical operations that are sensitive to quantization noise use higher precision or specialized handling, while less sensitive operations use aggressive quantization. This local differentiation maintains accuracy while achieving efficiency gains.
Solution Approach 2:
The patent performs preliminary quantization analysis and calibration before full training. By pre-computing quantization parameters and identifying sensitive operations, the system prepares optimal quantization strategies in advance, reducing the impact of quantization noise during actual training while maintaining efficiency.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for efficient quantized learning. A tensor is received at a layer of a neural network, and a current tensor is generated at a first bitwidth based on the received tensor. One or more quantization parameter values are determined based on the current tensor. The current tensor is quantized to a lower bitwidth based on one or more quantization parameter values determined based on a previous tensor generated during the training of a neural network.