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The presentation covers various machine learning tasks that can be performed on ESP32 chips, such as gesture recognition, keyword spotting, image recognition, object detection, and even running a minimal implementation of Large Language Models. The focus is on running inference directly on the hardware, and examples are demonstrated using various frameworks like Edge Impulse and TensorFlow Lite. The importance of proper data acquisition and sensor interfacing for accurate model training is emphasized, along with the challenges involved in adapting existing models to different hardware configurations.
Gesture recognition involves classifying sensor data, needing specific data acquisition code.
TensorFlow Lite for Microcontrollers faces challenges, currently poorly supported in Arduino.
Image recognition is executed using a trained MobileNet model for classifying images.
Object detection is optimized using the ESP32-S3's vector extensions for faster inference.
Running LLaMA on ESP32-S3 produces semi-coherent text, showcasing LLM capabilities.
Running machine learning inference directly on edge devices like the ESP32 exemplifies the growing importance of on-device AI, particularly for applications requiring low latency and reduced bandwidth. Data acquisition methods, as discussed, are critical to model effectiveness, emphasizing a need for robust preprocessing techniques. As we see a shift towards localized decision-making capabilities, the agility offered by platforms like Edge Impulse will facilitate rapid prototyping and deployment in IoT environments.
The challenges faced in adapting frameworks like TensorFlow Lite reflect the ongoing need for better support on resource-constrained devices. The mention of running Large Language Models, albeit limited, showcases an interesting trend towards exploring more complex AI functionalities in smaller architectures. As hardware continues to evolve, integrating AI models efficiently remains a key focus area for researchers and developers.
In the context of the video, inference is performed directly on the ESP32 chips to enable real-time responses from the models.
TensorFlow Lite is discussed as a tool to run models on small microcontrollers like the ESP32.
It is highlighted as a key resource for training models and deploying them on ESP32 boards.
The video illustrates using Edge Impulse for various machine learning tasks on ESP32 chips.
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TensorFlow Lite is mentioned as a framework to enable machine learning on microcontrollers.
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Brock Mesarich | AI for Non Techies 11month
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