Neuron-by-Neuron Quantization for Neural Network Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current quantization-aware training (QAT) methods for low-bit quantized neural networks suffer from significant accuracy loss and suboptimal results due to non-differentiable quantization operations and greedy layer-by-layer quantization approaches, which fail to recover lost information and lead to suboptimal models.
Innovation Solution
A new QAT approach that selectively quantizes and freezes subsets of components in each layer, using a minimum mean-square error optimization to determine quantization parameters, allowing for back-propagation of gradients and iterative retraining to improve accuracy, specifically employing filter-by-filter, neuron-by-neuron, and hybrid methods to optimize quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If low-bit quantization (2-bit, 3-bit) is applied to reduce resource consumption, then device efficiency and resource usage improve, but accuracy loss increases significantly
Solution Approach 1:
The patent segments the quantization process into multiple iterations, where in each iteration only a subset of layers is quantized and frozen, while other layers remain in full precision and continue training. This gradual segmentation allows the network to adapt to quantization effects progressively, reducing accuracy loss compared to applying low-bit quantization all at once.
Solution Approach 2:
The patent performs preliminary quantization on selected layers before finalizing the entire network quantization. By pre-quantizing specific layers, freezing them, and continuing to train other layers in full precision, the network prepares for low-bit operation gradually, allowing subsequent layers to adapt to the quantized representations and maintain accuracy.
2Ease of operation
If direct quantization method is used to simplify the training process, then ease of operation improves, but accuracy loss increases due to information loss
Solution Approach 1:
The patent maintains continuous training of unquantized layers while quantized layers are frozen, ensuring that the network continuously learns to compensate for quantization effects. This continuous useful action in the form of ongoing training in full precision preserves information that would otherwise be lost, while still progressing toward low-bit deployment.
Solution Approach 2:
The patent uses full-precision layers as intermediaries that process data between quantized layers. These intermediary layers in full precision act as buffers that preserve information and help the quantized layers adapt, reducing the direct impact of quantization information loss while maintaining the simplified low-bit structure where applicable.
3Device complexity
If layer-by-layer greedy quantization is applied to reduce computational complexity, then device complexity decreases, but accuracy deteriorates due to suboptimal quantization
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the network to adjust to quantization effects through continued training of unquantized layers. Rather than statically freezing all quantized layers immediately, the system dynamically balances between quantized and full-precision layers during training, allowing the network to adapt its representations and maintain accuracy while reducing complexity.
Solution Approach 2:
The patent changes the precision parameter dynamically during training - starting with full precision for all layers, then progressively changing selected layers to low-bit precision while keeping others in full precision. This parameter change strategy allows the network to adapt to precision reductions gradually, avoiding the suboptimal accuracy that results from greedy, static layer-by-layer quantization.
Data Source
AI summary
Quantization of a convolutional neural network (CNN) into a quantized neural network (QNN) reduces the computational resources required to operate the neural network, which is especially advantageous for operation of a neural network on resource-constrained devices. However, QNNs with low bit-widths suffer from significant losses in accuracies. Accordingly, approaches for quantization-aware training are disclosed that utilize component-by-component quantization during training to improve the accuracy of the resulting QNN. Component-by-component quantization may include filter-by-filter quantization, or preferably neuron-by-neuron quantization with some form of gradient forwarding.