AdaQAT: Adaptive Bit-Width Quantization-Aware Training for Efficient Deep Neural Network Inference

AdaQAT is an optimization-based method that automatically learns the optimal bit-widths for weights and activations during training, enabling efficient deployment of deep neural networks on edge devices.