Channel-Specific Neural Network Processing with Quantization
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Solution Overview
Problem
Existing neural networks face challenges in processing channels with varying value distributions, requiring channel-specific processing to effectively handle differences in scale factors across channels without dedicated hardware multipliers.
Innovation Solution
Implementing channel-specific quantization and compensation weights for neurons to align and process inputs from different channels, allowing for multiplication and accumulation operations without dedicated multipliers, and applying channel-specific quantization factors to ensure accurate output processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If channel-specific processing is implemented to handle varying value distributions, then processing accuracy is improved, but device complexity increases due to the need for dedicated hardware multipliers
Solution Approach 1:
The patent applies parameter changes by introducing channel-specific quantization factors and compensation weights that adapt to the value distribution of each channel. Instead of using dedicated hardware multipliers, the system modifies numerical parameters (quantization factors and weights) to compensate for scale differences across channels, achieving channel-specific processing with standard hardware operations
Solution Approach 2:
The patent replaces the mechanical hardware multiplier system with a computational approach using quantization and compensation weights. The dedicated hardware multipliers that would traditionally be needed for channel-specific scaling are substituted by software-based quantization operations and weight multiplication, reducing hardware complexity while maintaining processing accuracy
2Measurement precision
If dedicated hardware multipliers are used for channel-specific scaling, then computation accuracy is improved, but computation efficiency deteriorates due to increased hardware requirements
Solution Approach 1:
The patent implements universality by making the neural network processor capable of handling multiple channels with different value distributions using the same hardware architecture. Through channel-specific quantization factors and compensation weights, the system achieves multi-channel compatibility without requiring dedicated hardware for each channel, thereby improving computation efficiency while maintaining accuracy
3Measurement precision
If channel-specific quantization is applied to align scale factors, then processing accuracy is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent applies preliminary action by performing channel-specific quantization and computing compensation weights before the main neural network processing. The scale factors are aligned and compensation weights are pre-calculated in advance, so that during actual inference, the system only needs to apply these pre-computed weights through standard multiplication operations, reducing the complexity of real-time processing
Data Source
AI summary
A method for channel specific neural network processing includes receiving current layer multi-channel output descriptors by next layer neurons. The current layer multi-channel output descriptors are provided by neurons of the current layer. The current layer and the next layer belong to a neural network. The next layer neurons process the current layer multi-channel output descriptors to provide next layer multi-channel output descriptors. The processing includes multiplying the current layer multi-channel output descriptors by channel compensated weights of the next layer neurons to provide next layer products that compensate for estimated differences between scale factors associated with different channels of the current layer multi-channel output descriptors. The next layer products are quantized by applying next layer output channel specific quantization.