Leveraging the power of quantization
Quantization in machine learning, particularly in ASR, refers to reducing the precision of the model’s parameters. This is typically done by mapping the continuous range of floating-point values to a discrete set of values, often represented by integers. The primary goal of quantization is to decrease the model’s computational complexity and memory footprint, which is crucial for deploying ASR systems on devices with limited resources, such as mobile phones or embedded systems. Quantization is essential for several reasons:
- Reducing model size: Using lower precision to represent the model’s weights can significantly reduce the model’s overall size. This is particularly beneficial for on-device deployment, where storage space is at a premium.
- Improving inference speed: Lower precision arithmetic is faster on many hardware platforms, especially those without dedicated floating-point units. This can lead to faster...
