A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.NE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ShiftLIF maps membrane potentials to logarithmically spaced power-of-two spike levels, improving representational capacity in SNNs while keeping synaptic operations multiplier-free.
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Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.
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ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization
ShiftLIF maps membrane potentials to logarithmically spaced power-of-two spike levels, improving representational capacity in SNNs while keeping synaptic operations multiplier-free.