quantization-aware training methods
Quantization-aware training methods are techniques used to train deep neural networks that are amenable to deployment on low-precision hardware. They aim to optimize and minimize the loss in accuracy caused by reducing the precision of neural network weights and activations during inference. These methods involve adding additional training steps to fine-tune the model's weights and ensure that they remain quantization-friendly, enabling efficient execution on resource-constrained devices.
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