tinyML Summit 2022: Optimizing AutoML for the tinyML Future

tinyML Summit 2022
tinyML AutoML Session
Optimizing AutoML for the tinyML Future
Elias FALLON, Vice President for Machine Learning, Qeexo Co.

Automatic Machine Learning (AutoML) is a set of techniques applying optimization on top of machine learning hyperparameters to achieve the best ML performance. In traditional cloud-based machine learning/AI, that just means achieving the highest accuracy, often without regard for other metrics such as latency and power efficiency. In our tinyML community, we have the challenge that latency and power efficiency are often as important as absolute accuracy.

In the tinyML context, one key to achieving the best performance is taking full advantage of the unique capabilities of the inference hardware platform. AutoML tools and techniques have started to be more hardware-aware with ideas like quantization-aware training. But to truly optimize the overall system performance AutoML tools need to optimize across the full signal flow from the raw sensor to inference result.

Qeexo AutoML uses optimization and search techniques to select signal processing filters, feature extraction, and machine learning model optimizations, all aware of the unique hardware selected for the project. In this talk, we will describe the overall AutoML flow to incorporate full optimization across the sensor to inference flow. The automatic selection of signal processing filters for STMicroelectronics’ Machine Learning Core (MLC) will be detailed. The full flow for an activity detection wakeup model, executing on the MLC, as well as the expected power consumption and accuracy, will be demonstrated. AutoML tools and full signal flow optimization are key innovations to enabling the tinyML future.

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