ZeroQ: A Novel Zero Shot Quantization Framework (2001.00281v1)

Abstract: Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining during quantization. This is often not possible for applications with sensitive or proprietary data, e.g., due to privacy and security concerns. Existing zero-shot quantization methods use different heuristics to address this, but they result in poor performance, especially when quantizing to ultra-low precision. Here, we propose ZeroQ , a novel zero-shot quantization framework to address this. ZeroQ enables mixed-precision quantization without any access to the training or validation data. This is achieved by optimizing for a Distilled Dataset, which is engineered to match the statistics of batch normalization across different layers of the network. ZeroQ supports both uniform and mixed-precision quantization. For the latter, we introduce a novel Pareto frontier based method to automatically determine the mixed-precision bit setting for all layers, with no manual search involved. We extensively test our proposed method on a diverse set of models, including ResNet18/50/152, MobileNetV2, ShuffleNet, SqueezeNext, and InceptionV3 on ImageNet, as well as RetinaNet-ResNet50 on the Microsoft COCO dataset. In particular, we show that ZeroQ can achieve 1.71\% higher accuracy on MobileNetV2, as compared to the recently proposed DFQ method. Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0.5\% of one epoch training time of ResNet50 on ImageNet). We have open-sourced the ZeroQ framework\footnote{https://github.com/amirgholami/ZeroQ}.

ZeroQ: A Novel Zero Shot Quantization Framework

The paper "ZeroQ: A Novel Zero Shot Quantization Framework" introduces a new paradigm in the quantization of neural networks without requiring access to original training data. Traditional quantization techniques typically depend on some dataset knowledge or fine-tuning with a data subset. ZeroQ shifts this approach by generating layer-wise data that mimics the statistics of batch normalization layers, enabling zero-shot quantization.

Technical Summary

ZeroQ leverages the intrinsic properties of batch normalization present in pre-trained models. The authors propose to use these properties to generate a synthetic dataset that maintains the statistical characteristics of the original data. This innovation enables the conversion of models to lower precision without access to the initial dataset.

The framework's core components are:

• Data-Free Quantization: By generating representative activations, ZeroQ eliminates the need for training data, mitigating privacy concerns associated with data access.
• Layer-Wise Calibration: Each layer is individually calibrated using synthetic data, ensuring minimal degradation in model performance.
• Optimization Strategy: It employs an optimization-based approach to align the generated data with original batch normalization statistics.

Experimental Results

The empirical evaluation conducted in the paper demonstrates the efficacy of ZeroQ across various standard benchmarks and architectures, including ResNet, MobileNet, and InceptionV3. The results indicate:

• A marginal accuracy loss, often within 1% of the full-precision baseline.
• Superior performance compared to existing data-free quantization methods, thus validating the effectiveness of ZeroQ's synthetic data generation.

Discussion

The introduction of ZeroQ holds several implications for the development and deployment of neural networks in environments where data privacy or availability is a concern. The flexibility to quantize models without relying on dataset retraining or fine-tuning enhances the adaptability and scalability of AI models in real-world applications.

Theoretically, this work prompts further exploration into leveraging internal model parameters, such as batch normalization, to facilitate other methods of model optimization and compression. Moreover, it raises questions regarding the extent to which synthetic data can replace real data for model adaptation processes.

Future Directions

Future research can extend the ZeroQ framework in several promising directions:

• Exploring the use of alternative network statistics and regularization techniques to enhance synthetic data generation.
• Expanding ZeroQ's applicability to other model architectures and layers lacking batch normalization.
• Investigating the integration of ZeroQ within automated machine learning (AutoML) pipelines for quantization-aware neural architecture search (NAS).

Overall, the paper presents a solid advancement towards data-independent model quantization, providing a foundation for further innovations in efficient AI model deployment.