Add a comprehensive Jupyter notebook tutorial demonstrating TFLM's
compression pipeline using the MNIST dataset. The tutorial covers weight
clustering with TensorFlow Model Optimization toolkit, post-training
quantization, and TFLM's LUT-based compression.
Update documentation to reference the new tutorial from the main README,
Python interpreter guide, and compression documentation.
BUG=#2636
Co-authored-by: Esun Kim <veblush@google.com>
Enhance compress() function to automatically apply proper
FlatBuffer alignment after compression, eliminating the need for
users to manually run tflite_flatbuffer_align as a separate step.
Use the C++ alignment wrapper internally, as the Python
flatbuffers library doesn't respect force_align schema
attributes.
Keep the API unchanged - compress() still returns a bytearray,
but now the output is properly aligned for the TFLM interpreter.
Update documentation, and build dependencies of the Python
package.
BUG=#3125
Clarify the usage of `MicroContext::AllocateDecompressionScratchBuffer`
and`tflite::micro::GetTensorData` for handling decompressed tensor
data.
Add a section on alternate decompression memory regions,
explaining how to specify and use specialized memory for
decompression.
Update instructions for compressing models using a YAML
specification.
Simplify the model compression and alignment command examples.
Spin off new Generic Benchmark Application documentation.
BUG=part of #2636
