Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators

13 Oct 2019Ritchie ZhaoChristopher De SaZhiru Zhang

Outliers in weights and activations pose a key challenge for fixed-point quantization of neural networks. While outliers can be addressed by fine-tuning, this is not practical for machine learning (ML) service providers (e.g., Google, Microsoft) who often receive customers' models without the training data... (read more)

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