Low-Energy Physiologic Biomarker Machine-Learning Inference on a Wearable Device with a GAP9 RISCV Based Processor
Christopher L. FELTON, Development Engineer IV, Mayo Clinic SPPDG

The presentation will cover a top to bottom institutional framework to develop new machine-learning physiologic biomarkers, which covers a breadth of related topics culminating in the evaluation of machine-learning (ML) models on low-energy wearable prototypes. The presentation will give an overview of the human subject testing the Mayo Clinic develops to build datasets for training (Techentin, et al., 2019); the machine-learning approaches to extract new physiologic biomarkers or signatures; and an overview how the Mayo team uses the Greenwaves toolflow and the Greenwaves GAP9 target to design low-energy wearable prototypes to demonstrate wearable physiologic monitoring
concepts. The presentation will include a specific example(s), primarily an effort to build regenerative physiologic signal autoencoders and determine the feasibility of implementation on a low-energy wearable platform. Additionally, the performance of the ML models and the results of the tinyML toolflow to reduce the model’s memory and computation resources, as well as results on the mapping to the Greenwaves GAP9 processor on custom hardware will be presented.

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