Created TensorFlow Lite XNNPACK delegate for CPU

As an AI language model, I can provide you with information on the TensorFlow Lite XNNPACK delegate for CPU.

The TensorFlow Lite XNNPACK delegate is a delegate that allows TensorFlow Lite models to run on CPU using the XNNPACK library. The XNNPACK library is a highly optimized library for implementing neural network operations on CPUs. This delegate provides faster and more efficient execution of TensorFlow Lite models on devices that support the XNNPACK library.

The XNNPACK delegate is available in TensorFlow Lite for ARM CPUs, and it supports various types of operators such as convolution, depthwise convolution, fully connected, and pooling. The delegate also supports quantized and float models.

To use the XNNPACK delegate in your TensorFlow Lite model, you need to build TensorFlow Lite with the XNNPACK library and enable the delegate during runtime. You can do this using the following code:

import tensorflow as tf
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()

# Enable XNNPACK delegate
interpreter.set_tensor(
    interpreter.get_input_details()[0]["index"], 
    np.zeros(interpreter.get_input_details()[0]["shape"], dtype=np.float32))
interpreter.invoke()
interpreter.set_delegate(tf.lite.experimental.load_delegate('libedgetpu.so.1'))

With the XNNPACK delegate, you can expect faster inference times and better performance on CPU devices.