Theory

Mixed precision training and quantization aware training (QAT) are two key techniques that optimize the performance of deep learning models. Here’s a breakdown of both:

• Mixed Precision Training: Mixed precision training involves using both 16-bit (half-precision) and 32-bit (single-precision) floating-point arithmetic during model training. By using 16-bit precision for certain operations, we can reduce memory usage and speed up computations, resulting in faster training times without sacrificing the model’s accuracy. This technique takes advantage of hardware accelerators like NVIDIA Tensor Cores, which are optimized for 16-bit computations, to boost performance.
• Quantization Aware Training (QAT): Quantization aware training (QAT) involves simulating lower precision during training by converting the model’s weights and activations into smaller, more efficient formats, such as 8-bit integers. The model is trained to adapt to the quantized format, ensuring that performance remains high despite the reduced precision. This technique is especially important for deploying models on edge devices, such as mobile phones and embedded systems, where computational power and memory are limited.

By using mixed precision and quantization aware training, we can significantly reduce the size of the model, speed up the training process, and improve inference performance—all without a notable loss in accuracy. These techniques are essential for deploying deep learning models in real-world applications with limited resources.