UIESNN is a scale-aware spiking network that adds hierarchical multi-scale pooling to membrane dynamics in a residual architecture, achieving state-of-the-art results among SNN methods on EUVP and LSUI benchmarks.
Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based opti- mization to spiking neural networks
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SPIKER-LL extends the open-source Spiker+ SNN accelerator with microarchitectural support for the STSF local learning rule, delivering up to 93% accuracy, sub-millisecond latency, and under 0.1 mJ per inference on MNIST variants while remaining DSP-free.
A neuromorphic edge system using event vision and sparse SNNs on Loihi 2 achieves up to 84% F1 score at 90 mW for privacy-preserving fall detection.
citing papers explorer
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UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement
UIESNN is a scale-aware spiking network that adds hierarchical multi-scale pooling to membrane dynamics in a residual architecture, achieving state-of-the-art results among SNN methods on EUVP and LSUI benchmarks.
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Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks
SPIKER-LL extends the open-source Spiker+ SNN accelerator with microarchitectural support for the STSF local learning rule, delivering up to 93% accuracy, sub-millisecond latency, and under 0.1 mJ per inference on MNIST variants while remaining DSP-free.
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Privacy-preserving fall detection at the edge using Sony IMX636 event-based vision sensor and Intel Loihi 2 neuromorphic processor
A neuromorphic edge system using event vision and sparse SNNs on Loihi 2 achieves up to 84% F1 score at 90 mW for privacy-preserving fall detection.