# Hardware/Mobile {% embed url="" %} Hardware and Mobile - Testing and Deployment {% endembed %} ## Summary * Embedded and mobile devices have low-processor with little memory, which makes the process slow and expensive to compute. Often, we can try some tricks such as reducing network size, quantizing the weights, and distilling knowledge. * Both **pruning** and **quantization** are model compression techniques that make the model physically smaller to save disk space and make the model require less memory during computation to run faster. * **Knowledge distillation** is a compression technique in which a small “student” model is trained to reproduce the behavior of a large “teacher” model. * Embedded and mobile PyTorch/TensorFlow frameworks are less fully featured than the full PyTorch/TensorFlow frameworks. Therefore, we have to be careful with the model architecture. An alternative option is using the interchange format. * **Mobile machine learning frameworks** are regularly in flux: Tensorflow Lite, PyTorch Mobile, CoreML, MLKit, FritzAI. * The best solution in the industry for **embedded** devices is NVIDIA. * The **Open Neural Network Exchange** (ONNX for short) is designed to allow framework interoperability. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/hardware-mobile.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.