The AXE framework can be extended to support more flexible quantization schemes, such as mixed precision or channel-wise quantization, by incorporating adaptive mechanisms that allow for varying bit widths across different layers or channels of a neural network. This can be achieved through the following strategies:
Dynamic Bit Width Allocation: Instead of enforcing a uniform bit width across all layers, AXE can be modified to dynamically allocate bit widths based on the sensitivity of each layer to quantization errors. For instance, layers that are more critical for model performance could be assigned higher precision, while less critical layers could utilize lower precision. This approach can be guided by analyzing the contribution of each layer to the overall model accuracy during the quantization process.
Channel-wise Quantization: The framework can be adapted to allow for channel-wise quantization, where each channel in a convolutional layer can have its own quantization parameters. This would involve extending the accumulator-aware constraints to operate on a per-channel basis, ensuring that the overflow avoidance guarantees are maintained for each channel independently. This flexibility can lead to improved model performance, especially in architectures like convolutional neural networks (CNNs) where different channels may exhibit varying distributions of activations.
Integration with Existing Mixed Precision Techniques: AXE can be integrated with existing mixed precision training techniques, which often utilize a combination of lower precision for certain operations while maintaining higher precision for others. By leveraging the insights from accumulator-aware quantization, AXE can ensure that the mixed precision operations do not lead to overflow, thus maintaining the integrity of the computations.
Adaptive Learning Rates for Quantization Parameters: The framework can incorporate adaptive learning rates for quantization parameters, allowing for more granular control over the quantization process. This would enable the model to learn optimal quantization strategies during the post-training quantization phase, further enhancing the flexibility of the quantization scheme.
By implementing these strategies, the AXE framework can effectively support a broader range of quantization schemes, enhancing its applicability across various deep learning models and tasks.