Robust Quantization: One Model to Rule Them All

Understand

Neural network quantization methods often involve simulating the quantization process during training, making the trained model highly dependent on the target bit-width and precise way quantization is performed.

• Robust quantization offers an alternative approach with improved tolerance to different classes of data-types and quantization policies.
• It opens up new exciting applications where the quantization process is not static and can vary to meet different circumstances and implementations.
• To address this issue, we propose a method that provides intrinsic robustness to the model against a broad range of quantization processes.