2 citations · 2 across the 2 of their papers we have counts for
2 papers
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.ET2020
Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse
Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +6
The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a major lead for reducing the energy consum…