Memristive networks exhibit biological-like population spiking and nonlinear resonance maximized when input frequency matches the network's intrinsic timescale, with optimal computation frequency just before resonance onset.
Training spiking neural networks using lessons from deep learning,
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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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Intrinsic Neuro-Synaptic Spiking Dynamics and Resonance in Memristive Networks
Memristive networks exhibit biological-like population spiking and nonlinear resonance maximized when input frequency matches the network's intrinsic timescale, with optimal computation frequency just before resonance onset.
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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.