Any-Precision Deep Neural Networks (1911.07346v2)

Abstract: We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-widths, by truncating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learning models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures and applicable to multiple vision tasks. Our code is released at https://github.com/SHI-Labs/Any-Precision-DNNs.

• The paper presents a novel contribution by enabling a single DNN to operate at various numerical precision levels without retraining.
• It employs quantization-aware training and knowledge distillation to maintain robust accuracy across full to low precision configurations.
• Extensive experiments on multiple architectures and datasets confirm the method’s efficiency and broad applicability in diverse real-world scenarios.

Overview of Any-Precision Deep Neural Networks

This paper introduces an innovative approach to designing and training deep neural networks (DNNs) called any-precision DNNs. The primary contribution is a methodology that enables these models to be dynamically adjusted at runtime to different numerical precision levels without the need for retraining or fine-tuning, making them suitable for various real-world applications that require an adaptable trade-off between computational efficiency and model accuracy.

Summary of Contributions

The authors present a flexible framework that permits a single model to operate under multiple precision levels, from full precision to very low precision, by simply quantizing the model's layers' weights and activations. Their focus is on tackling the gap in existing methodologies where models are typically trained and optimized individually for specific efficiency/accuracy trade-off points. The proposed method utilizes advances in quantization-aware training, dynamically changing the network's numerical precision during the training phase, ensuring that the model remains robust and accurate across different precision settings during inference.

Key contributions and findings of this research include:

1. Any-Precision Capability: The proposed framework allows for smooth transitions and flexible adjustments between precision levels, demonstrating that a single trained model can provide accuracy comparable to models dedicatedly retrained for each precision level.
2. Practical Implication: This characteristic reduces the need for maintaining and switching between multiple models, significantly saving storage resources, which is particularly advantageous when deployed under varying resource constraints.
3. Model-Agnostic Implementation: The approach is confirmed to be architecture-agnostic across various DNN architectures and validated with multiple computer vision tasks, showcasing broad applicability.
4. Knowledge Distillation: Enhanced performance in low-bit settings is achieved by incorporating knowledge distillation where outputs from higher precision models guide lower precision models during training.
5. Experiments and Results: The paper extensively evaluates the model on datasets such as Cifar-10, SVHN, and ImageNet using architectures like Resnet, AlexNet, and MobileNet, further extending to image segmentation tasks, thereby supporting the framework's effectiveness across diverse scenarios.

Implications of Research

The implications of this research are significant both practically and theoretically:

• Scale and Flexibility in Deployment: By mitigating the requirement to re-train or recalibrate models for varying precision needs, the method paves the way for more adaptive deployment strategies suitable for hardware with diverse performance profiles and power limitations.
• Future of AI and Edge Computing: This flexibility aligns with the growing need for adaptable AI models in edge computing where computational resources and energy efficiency are critical constraints.
• Directions for Future Research: This framework could spur further investigation into better optimization methods for quantized models and inspire new quantization techniques that further bridge the accuracy-efficiency gap, especially as new neural network architectures and tasks emerge.

Conclusion

The concept of any-precision DNNs marks an advancement in how deep learning models can be utilized in resource-varying environments. The promising results indicate not just an improvement over single-point solutions but also an approach that enhances robustness and accessibility of deep learning systems. The research offers a template for balancing the challenges of designing neural networks capable of adaptable precision, suggesting a trajectory that could influence future developments in efficient AI deployment.