Presentation
Late-Breaking Results: Ultra Energy Efficient Personalized Glucose Prediction with RLS Implementation on a Tiny FPGA
DescriptionAccurate prediction of future blood glucose (BG) levels is critical for
the effective management of type 1 diabetes. Existing approaches
for glucose prediction often rely on deep learning models that are
computationally intensive. In this work, we propose an adaptive and
lightweight recursive least squares (RLS)-based BG predictor that
enables online model updates while maintaining low computational
complexity. Our RLS implementation on a tiny FPGA delivers 1.55
𝜇s latency and 51.3 𝜇J energy consumption per prediction. Evalua-
tion on the OhioT1DM dataset demonstrates an RMSE of 18.83 and
32.12 for 30 and 60 minute prediction horizons, respectively. Our
FPGA BG predictor outperforms the state-of-the-art (SoA) far-edge
implementations across all metrics: latency, energy, and error.
the effective management of type 1 diabetes. Existing approaches
for glucose prediction often rely on deep learning models that are
computationally intensive. In this work, we propose an adaptive and
lightweight recursive least squares (RLS)-based BG predictor that
enables online model updates while maintaining low computational
complexity. Our RLS implementation on a tiny FPGA delivers 1.55
𝜇s latency and 51.3 𝜇J energy consumption per prediction. Evalua-
tion on the OhioT1DM dataset demonstrates an RMSE of 18.83 and
32.12 for 30 and 60 minute prediction horizons, respectively. Our
FPGA BG predictor outperforms the state-of-the-art (SoA) far-edge
implementations across all metrics: latency, energy, and error.
Event Type
Late Breaking Results
TimeMonday, July 276:50pm - 6:53pm PDT
LocationExhibit Hall
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