Understand the TFLite file format: The TFLite file format is a binary file that contains the
model's metadata, such as input and output shapes and data types, as well as the model's graph
and weights. We need to understand the structure of this file format to be able to parse and
interpret it.
Parse the TFLite file: Write code to read and parse the TFLite file, extracting the metadata
and the model's graph and weights. This involves using low-level programming techniques
such as bit manipulation and memory management.
Load the model graph: Once TFLite file is parsed, we can load the model's graph into
memory. The graph consists of a set of nodes that represent mathematical operations, such as
matrix multiplication and convolution, and edges that connect these nodes and represent the
flow of data between them.
Execute the model graph: With the model graph loaded into memory, you can execute it on
the target hardware platform. This involves using hardware-specific optimizations, such as
using vectorized instructions on CPUs or using specialized hardware accelerators, to speed
up the computation.
Post-processing: After executing the model graph, we may need to perform additional postprocessing steps to convert the output data into a format that is usable by the target
application.