258 citations · 352 across the 12 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…
cs.NE2020★ 258 cited
Spiking Neural Networks Hardware Implementations and Challenges: a Survey
Maxence Bouvier, Alexandre Valentian, Thomas Mesquida +4
Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing c…