Hailo Integration for Numerical Data: Workflow for SQLite and Machine Learning on Raspberry Pi 5

General
1

Project Goal:
We are building a workflow to:

  1. Process numerical data extracted from an SQLite database.
  2. Analyze the data using Machine Learning models optimized for the Hailo AI Accelerator.
  3. Save the results back into the SQLite database.

Current Status:

  • We have successfully implemented and tested the pipeline using a TensorFlow-based dummy model for processing data in batches. :heavy_check_mark:
  • HailoRT 4.19.0 is installed and recognizes the Hailo device (Device(‘0000:01:00.0’)). :heavy_check_mark:
  • Benchmarks show excellent performance with an existing HEF file (image-processing). :heavy_check_mark:
  • However, the current HEF model is image-based and incompatible with our numerical data workflow. :x:

Challenges Faced:

  1. Compiler Missing:
  • The installed HailoRT version (4.19.0) does not include the TensorFlow-to-HEF compiler.
  • Commands like:

python

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hailortcli compile-tensorflow

→ Are not available.
2. Hailo Model Zoo Installation Issue:

  • Attempting to install the Hailo Model Zoo 2.13.0 (Python package) in our Python 3.11 environment fails with:

yaml

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fatal error: longintrepr.h: No such file or directory
  • The lap dependency seems incompatible with Python 3.11.
  1. Workflow Model Compatibility:
  • Current VStreams in the example HEF are image-based (NHWC 416x416x3) and unsuitable for tabular input like our numerical features (3 values per row).
  • Unsure how to adapt the pipeline for numerical data processing.

Questions for the Community:

  1. Is there an example or template for integrating numerical data models (e.g., DNNs) with the Hailo AI Accelerator?
  2. Should we downgrade to Python 3.9 for compatibility with the Model Zoo, or is there an alternative?
  3. Are there any recommended workflows or compiler tools for non-image-based processing?
  4. Can TAPPAS be used to streamline this kind of numerical data pipeline?

Additional Info:

  • Raspberry Pi 5 running 64-bit Raspberry Pi OS.
  • TensorFlow 2.18.0 installed and working in a virtual environment.
  • SQLite database for input and output, with batch processing already validated.

Any advice, examples, or insights would be greatly appreciated!