Level up your TinyML skills

What I do, How I do it

Memory-efficient porting of scikit-learn classifiers

Even though a few transpiler already exists to port Machine Learning models from Python to C/C++, none is optimized for resource-constrained hardware like MCUs.

I created RAM-efficient, Flash-efficient implementations of a few of them with the aim to run even on Attiny hardware!

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    Memory efficient

    Most classifiers require less than 1 Kb of RAM to run. Tree-based models can fit even in 100 bytes of RAM!

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    Space efficient

    When the model requires space (SVM, PCA), I re-arranged to code to leverage variadic functions and circumvent resource constraints

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    Blazing fast

    You can run inference on 100+ features at sub-milliseconds speed

Embedded Computer Vision

I implemented a few image-processing and computer vision algorithms hand-crafted to run on the Esp32-cam hardware as fast as possible, while still fitting in the limited RAM available

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    Motion detection

    Fully configurable sensitivity, definition of custom regions-of-interest, visually debuggable in the browser

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    HOG transform

    To perform simple real-time object recnognition, you may not need a TensorFlow CNN. Sometimes simpler is better (and 100x faster)!

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    Color tracking

    A color-blob localization algorithm to track the position of custom objects in real-time

Custom software and tools

If your company needs custom software and tools for data acquisition, cleaning and management, I create those tools for you. Already tried and tested by a Fortune 100 organization, so you're in good company

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    Custom model creation

    If you have a specific use case and can't find a fit with the solutions already on the market, I create one tailored for you

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    Data collection

    Do you need to collect data remotely over BLE or WiFi? I got you covered

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    Data labelling and visualization

    It is often said that in Machine Learning 'Garbage in, Garbage out'. I throw the garbage out of your pipeline

TensorFlow-free advocate

When the industry went all-in on TensorFlow Lite for Microcontrollers, I went the opposite direction.

While recognizing that Tf has it's scope in the embedded world, I advocate that scope is much more limited that it is depicted nowadays

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    Sub-millisecond inference

    XGBoost can produce state-of-the-art performance on many tasks at a fraction of the execution time of Tf

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    Truly Tiny

    512 Kb of RAM is tiny compared to GPU-based networks, but it's not so tiny compared to the wide available hardware on the market today

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    Did you know you can run XGBoost on the Attiny85? Check the post

My public work

  • EloquentTinyML: a boilerplate-free Arduino library to use TensorFlow on MCUs
  • EloquentEsp32Cam: Arduino library to use the Esp32-cam like an expert
  • micromlgen: port scikit-learn classifiers from Python to optimized C++
  • tinymlgen: port TensorFlow models from Python to C++
  • everywhereml: data loading, processing and classification for Python to be ported to C++

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