624 citations · 1.2k across the 19 of their papers we have counts for
19 papers
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…
Efficient Synaptic Delay Implementation in Digital Event-Driven AI Accelerators
Roy Meijer, Paul Detterer, Amirreza Yousefzadeh +8
Synaptic delay parameterization of neural network models have remained largely unexplored but recent literature has been showing promising results, suggesting the delay parameteriz…
Roadmap to Neuromorphic Computing with Emerging Technologies
Adnan Mehonic, Daniele Ielmini, Kaushik Roy +50
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently util…
Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors
Alberto Patino-Saucedo, Roy Meijer, Amirreza Yousefzadeh +6
Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite th…
Spin Hall Nano-Oscillator Empirical Electrical Model for Optimal On-chip Detector Design
Rafaella Fiorelli, Mona Rajabali, Roberto Méndez-Romero +7
As nascent nonlinear oscillators, nano-constriction spin Hall nano-oscillators (SHNOs) represent a promising potential for integration into more complicated systems such as neural…
Time series segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro
Gabriel Galeote-Checa, Gabriella Panuccio, Angel Canal-Alonso +2
Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Around 30-40% of patients do not respond to pharmacological treatment, leading…
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 Memristor-Inspired Computation for Epileptiform Signals in Spheroids
Iván Díez de los Ríos, John Wesley Ephraim, Gemma Palazzolo +3
In this paper we present a memristor-inspired computational method for obtaining a type of running spectrogram or fingerprint of epileptiform activity generated by rodent hippocamp…
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…
Effect of Device Mismatches in Differential Oscillatory Neural Networks
Jafar Shamsi, María José Avedillo, Bernabé Linares-Barranco +1
Analog implementation of Oscillatory Neural Networks (ONNs) has the potential to implement fast and ultra-low-power computing capabilities. One of the drawbacks of analog implement…
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…
Neutron-Induced, Single-Event Effects on Neuromorphic Event-based Vision Sensor: A First Step Towards Space Applications
Seth Roffe, Himanshu Akolkar, Alan D. George +2
This paper studies the suitability of neuromorphic event-based vision cameras for spaceflight, and the effects of neutron radiation on their performance. Neuromorphic event-based v…
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian +4
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorit…
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…
A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing
F. Alibart, S. Pleutin, O. Bichler +4
A large effort is devoted to the research of new computing paradigms associated to innovative nanotechnologies that should complement and/or propose alternative solutions to the cl…