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Honey, I shrunk the TinyML | Lars Gregori
Discover how TinyML enables machine learning on microcontrollers, reducing complexity and size to run locally without cloud processing, using the Raspberry Pi Pico as a demonstration.
- The talk revolves around TinyML, a technology that enables machine learning to run on microcontrollers, which is small, low-power, and low-cost.
- The Raspberry Pi Pico is a good example of a microcontroller that can run TinyML, having an ARM Cortex M0+ processor and 264KB of RAM.
- The first example shown is an XOR model, which is a simple machine learning model that can be implemented on the Raspberry Pi Pico.
- The Pico is programmed using MicroPython, a variant of Python that is optimized for microcontrollers.
- The TensorFlow Lite interpreter is used to convert the machine learning model into a binary that can be run on the microcontroller.
- The key advantage of TinyML is that it allows machine learning to be run locally on the microcontroller, without the need for an internet connection or cloud processing.
- The talk highlights the importance of reducing the size and complexity of machine learning models to enable them to run on microcontrollers.
- The Pico has a limited amount of memory and processing power, which requires the model to be optimized and quantized to run efficiently.
- The optimization process involves reducing the number of layers and nodes in the model, and using quantization to reduce the precision of the model’s weights and biases.
- The talk concludes that TinyML is a promising technology that has the potential to enable machine learning to be used in a wide range of applications, from home automation to wearables to Internet of Things (IoT) devices.