Efficient Mixed-Precision Quantization of Deep Neural Networks for Edge Applications Thesis Title: Efficient Mixed-Precision Quantization of Deep Neural Networks for Edge Applications This thesis explores the impact of mixed-precision quantization (MPQ) on deploying deep neural networks (DNNs) in edge applications. The research aims to reduce computational complexity during inference on embedded devices by simplifying scaling factors to powers of two, enabling efficient shift operations in place of multiplications. This approach reduces computational costs and energy consumption but can narrow the quantization range, potentially affecting model performance. The study involved training various models, including MobileNetV1, MobileNetV2, an auto-encoder, EfficientNet, ResNet, and a CNN for a keyword spotting (KWS) task.