258 citations · 352 across the 12 of their papers we have counts for
11 papers · 1 filter
A CMOL-Like Memristor-CMOS Neuromorphic Chip-Core Demonstrating Stochastic Binary STDP
L. A. Camuñas-Mesa, E. Vianello, C. Reita +2
The advent of nanoscale memristors raised hopes of being able to build CMOL (CMOS/nanowire/moLecular) type ultra-dense in-memory-computing circuit architectures. In CMOL, nanoscale…
Experimental demonstration of Single-Level and Multi-Level-Cell RRAM-based In-Memory Computing with up to 16 parallel operations
E. Esmanhotto, T. Hirtzlin, N. Castellani +7
Crossbar arrays of resistive memories (RRAM) hold the promise of enabling In-Memory Computing (IMC), but essential challenges due to the impact of device imperfection and device en…
Experimental Body-input Three-stage DC offset Calibration Scheme for Memristive Crossbar
Charanraj Mohan, L. A. Camuñas-Mesa, Elisa Vianello +4
Reading several ReRAMs simultaneously in a neuromorphic circuit increases power consumption and limits scalability. Applying small inference read pulses is a vain attempt when offs…
PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials
Yigit Demirag, Filippo Moro, Thomas Dalgaty +5
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the…
Embracing the Unreliability of Memory Devices for Neuromorphic Computing
Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein +4
The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near- or in-memory. However, this type of computation is not compatible with c…
Implementation of Ternary Weights with Resistive RAM Using a Single Sense Operation per Synapse
Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +5
The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a significant lead for reducing the energy…