EMEA 2021 tiny Talks: Building Heterogeneous TinyML Pipelines

EMEA 2021 Building Heterogeneous TinyML Pipelines Christopher KNOROWSKI, CTO, SensiML Corp When complexity and/or the number of the class in the data increases, creating a successful model becomes more challenging in constrained embedded devices. In order to overcome this challenge, we can iteratively combine the classes into similar groups and optimize for each new group using different classifiers and/or features. This creates a hierarchical model, which is a combination of several simple and high-performing models. Hierarchical models often provide a better resource vs. accuracy performance than a single large model. In this talk we will describe an embedded SDK architecture that makes it possible to combine mixed classifier machine learning pipelines efficiently into a single library. The SDK allows for the creation of hierarchical and multi-model machine learning pipelines while reducing the overall memory footprint.

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