A systolic-array CNN accelerator on the Lattice iCE40UP5K FPGA achieves 98% validation accuracy for SCG feature classification while using 8.55 mW, 95.5 ms inference time, 2,861 LUTs, and 7 DSP blocks.
The NASA twins study: A multidimensional analysis of a year-long human spaceflight
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At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts
A systolic-array CNN accelerator on the Lattice iCE40UP5K FPGA achieves 98% validation accuracy for SCG feature classification while using 8.55 mW, 95.5 ms inference time, 2,861 LUTs, and 7 DSP blocks.