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.
Identify- ing gravity-related artifacts on ballistocardiography signals by comparing weightlessness and normal gravity recordings (ARTIFACTS): Protocol for an observational study
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.