Has anyone integrated ai models in to micropython?

 Integrating AI models into MicroPython can be challenging due to the limited resources available on microcontrollers, which usually have constraints on memory, processing power, and energy consumption. However, there are examples of AI models being integrated into MicroPython, particularly for TinyML applications.

One approach is to use TensorFlow Lite for Microcontrollers, a version of TensorFlow Lite designed specifically for microcontrollers and embedded devices. TensorFlow Lite for Microcontrollers supports a subset of operations available in the full TensorFlow Lite, making it suitable for running on resource-constrained devices.

While TensorFlow Lite for Microcontrollers does not have direct support for MicroPython, you can use the C++ API to integrate the AI models into your MicroPython application. Some microcontrollers, like the ESP32, support both MicroPython and the TensorFlow Lite for Microcontrollers C++ API. In such cases, you can use the C++ API to run the AI model and then interact with it through MicroPython.

An example of this approach can be found in the following GitHub repository: https://github.com/mocleiri/tensorflow-micropython-examples

It demonstrates how to use TensorFlow Lite for Microcontrollers with MicroPython on an ESP32 microcontroller. The repository includes examples for running speech recognition and image classification models.

Keep in mind that running AI models on microcontrollers can be challenging due to the limited resources available. You may need to optimize the model, use quantization, or apply other techniques to make the AI model suitable for deployment on such devices.

Is there way to convert a onnx to tinyml, or tensorflow lite?

 Yes, you can convert an ONNX model to TensorFlow Lite (TFLite), which can then be used for TinyML applications. The conversion process typically involves two steps:

1. Convert ONNX model to TensorFlow model: You can use the onnx-tf package, which provides a converter to transform ONNX models into TensorFlow models. First, you need to install the onnx-tf package:

Yes, you can convert an ONNX model to TensorFlow Lite (TFLite), which can then be used for TinyML applications. The conversion process typically involves two steps:

1. Convert ONNX model to TensorFlow model: You can use the onnx-tf package, which provides a converter to transform ONNX models into TensorFlow models. First, you need to install the onnx-tf package:

bash
pip install onnx-tf

Then, you can use the following Python code to convert your ONNX model to a TensorFlow model:

python
import onnx
from onnx_tf.backend import prepare

# Load the ONNX model
onnx_model = onnx.load("path/to/your/model.onnx")

# Convert the ONNX model to TensorFlow model
tf_model = prepare(onnx_model)

# Export the TensorFlow model to a protobuf file
tf_model.export_graph("path/to/output/tensorflow_model.pb")

2. Convert TensorFlow model to TensorFlow Lite model: Once you have the TensorFlow model, you can use the TensorFlow Lite converter to convert it to a TFLite model. Install TensorFlow, if you haven't already:

bash
pip install tensorflow

Then use the following Python code to convert the TensorFlow model to a TFLite model:

python
import tensorflow as tf

# Load the TensorFlow model
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
    "path/to/output/tensorflow_model.pb",
    input_arrays=["input_node_name"],
    output_arrays=["output_node_name"],
)

# Convert the TensorFlow model to a TFLite model
tflite_model = converter.convert()

# Save the TFLite model
with open("path/to/output/tflite_model.tflite", "wb") as f:
    f.write(tflite_model)

Replace "input_node_name" and "output_node_name" with the names of the input and output nodes of your model. You can use tools like Netron to visualize your model and find the input and output node names.

Once you have the TFLite model, you can deploy it to microcontrollers or other edge devices for TinyML applications. Please note that you might need to perform additional optimization, quantization, or model pruning to make the model suitable for deployment on resource-constrained devices. TensorFlow Lite provides tools and techniques for model optimization, which can be found in the official documentation.

Google Edge TPU Device

https://aiyprojects.withgoogle.com/edge-tpu/

EDGE TPU MODULE (SOM) SPECIFICATIONS
CPU NXP i.MX 8M SOC (quad Cortex-A53, Cortex-M4F)
GPU Integrated GC7000 Lite Graphics
ML accelerator Google Edge TPU coprocessor
RAM 1 GB LPDDR4
Flash memory 8 GB eMMC
Wireless Wi-Fi 2x2 MIMO (802.11b/g/n/ac 2.4/5GHz)
Bluetooth 4.1
Dimensions 40 mm x 48 mm

TensorFlow Lite

The Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing for low-power devices. For example, it can concurrently execute multiple state-of-the-art vision models on high-res video at 30+ fps, in a power efficient manner.
With one of the following Edge TPU devices, you can build embedded systems with on-device AI features that are fast, secure, and power efficient.