most citedHardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks

2 citations · 2 across the 1 of their papers we have counts for

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cs.NE2022★ 2 cited

Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks

Filippo Moro, E. Esmanhotto, T. Hirtzlin +10

Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computat…