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When Does Compensating Inherited Quantization Error Help? Mean-Square Guarantees for Layer-Wise Quantization and Their Limits
TL;DR
Layer-wise post-training quantization can effectively compensate for inherited quantization errors in neural networks, ensuring no greater output error than without compensation. This approach shows promise, particularly in linear networks, but has limitations with non-linear activations.
✦ Why It Matters
Consider implementing layer-wise compensation in your quantization strategy to improve model performance.
Key Takeaways
- Use layer-wise compensation to mitigate quantization errors.
- Ensure calibration Gram matrices are invertible for best results.
- Be cautious with non-linear activations like ReLU.
- Experiment with fixed compensation strengths for optimal performance.
How It Works
Layer-wise compensation adjusts the quantization error from previous layers by applying a fixed strength to the compensation, which can reduce the overall output error in linear networks. The method relies on the properties of the calibration Gram matrices and the statistical characteristics of rounding errors.