SNNs with frequency-matched LIF decay factors achieve 6.22% higher test accuracy and 3.64x lower energy than ANN baselines on four mmWave datasets by using inherent low-pass filtering to suppress noise.
(84), the cutoff admits the closed form ωc(β) = arccos x(β) , x(β)≜ 4β−1−β 2 2β .(89) For β∈[3−2 √ 2,1) , we have x(β)∈[−1,1] , so ωc(β) is well-defined
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Frequency Matching in Spiking Neural Networks for mmWave Sensing
SNNs with frequency-matched LIF decay factors achieve 6.22% higher test accuracy and 3.64x lower energy than ANN baselines on four mmWave datasets by using inherent low-pass filtering to suppress noise.