624 citations · 1.2k across the 19 of their papers we have counts for
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Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators
Wooseok Choi, Tommaso Stecconi, Donato Francesco Falcone +14
Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in-memory inference and trainin…
Empirical study on the efficiency of Spiking Neural Networks with axonal delays, and algorithm-hardware benchmarking
Alberto Patiño-Saucedo, Amirreza Yousefzadeh, Guangzhi Tang +3
The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANN…
A Fully Digital Relaxation-Aware Analog Programming Technique for HfOx RRAM Arrays
Hamidreza Erfanijazi, Luis A. Camuñas-Mesa, Elisa Vianello +2
For neuromorphic engineering to emulate the human brain, improving memory density with low power consumption is an indispensable but challenging goal. In this regard, emerging RRAM…
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…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
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…
Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices
C. Mohan, L. A. Camuñas-Mesa, J. M. de la Rosa +2
The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to imple…
MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems
Corey Lammie, Wei Xiang, Bernabé Linares-Barranco +1
Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using…
Integration of nanoscale memristor synapses in neuromorphic computing architectures
Giacomo Indiveri, Bernabe Linares-Barranco, Robert Legenstein +2
Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largel…