Examinando por Autor "Hurtado, Antonio"

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Brain-inspired nanophotonic spike computing: challenges and prospects
(IOP Publishing, 2023-07-14) Romeira, Bruno; Adão, Ricardo; Nieder, Jana B.; Al-Taai, Qusay Raghib Ali; Zhang, Weikang; Hadfield, Robert H.; Wasige, Edward; Hejda, Matěj; Hurtado, Antonio; Malysheva, Ekaterina; Dolores-Calzadilla, Victor; Lourenço, João; Alves, David Castro; Figueiredo, José; Ortega-Piwonka, Ignacio; Javaloyes, Julien J. P.; Edwards, Stuart; Davies, J. Iwan; Horst, Folkert; Offrein, Bert J.
Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III–V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.
Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing
(American Physical Society, 2022-02-02) Hejda, Matěj; Alanis, Juan Arturo; Ortega-Piwonka, Ignacio; Lourenço, João; Figueiredo, José; Javaloyes, Julien J. P.; Romeira, Bruno; Hurtado, Antonio
n this work, we introduce an interconnected nano-optoelectronic spiking artificial neuron emitter-receiver system capable of operating at ultrafast rates (about 100 ps/optical spike) and with low-energy consumption (< pJ/spike). The proposed system combines an excitable resonant tunneling diode (RTD) element exhibiting negative differential conductance, coupled to a nanoscale light source (forming a master node) or a photodetector (forming a receiver node). We study numerically the spiking dynamical responses and information propagation functionality of an interconnected master-receiver RTD node system. Using the key functionality of pulse thresholding and integration, we utilize a single node to classify sequential pulse patterns and perform convolutional functionality for image feature (edge) recognition. We also demonstrate an optically interconnected spiking neural network model for processing of spatiotemporal data at over 10 Gbit/s with high inference accuracy. Finally, we demonstrate an off-chip supervised learning approach utilizing spike-timing-dependent plasticity for the RTD-enabled photonic spiking neural network. These results demonstrate the potential and viability of RTD spiking nodes for low footprint, low-energy, high-speed optoelectronic realization of spike-based neuromorphic hardware.
Spike propagation in a nanolaser-based optoelectronic neuron
(Optica Publishing Group, 2022-07-01) Ortega-Piwonka, Ignacio; Hejda, Matěj; Alanis, Juan Arturo; Lourenço, João; Hurtado, Antonio; Figueiredo, José; Romeira, Bruno; Javaloyes, Julien J. P.
With the recent development of artificial intelligence and deep neural networks, alternatives to the Von Neumann architecture are in demand to run these algorithms efficiently in terms of speed, power and component size. In this theoretical study, a neuromorphic, optoelectronic nanopillar metal-cavity consisting of a resonant tunneling diode (RTD) and a nanolaser diode (LD) is demonstrated as an excitable pulse generator. With the proper configuration, the RTD behaves as an excitable system while the LD translates its electronic output into optical pulses, which can be interpreted as bits of information. The optical pulses are characterized in terms of their width, amplitude, response delay, distortion and jitter times. Finally, two RTD-LD units are integrated via a photodetector and their feasibility to generate and propagate optical pulses is demonstrated. Given its low energy consumption per pulse and high spiking rate, this device has potential applications as building blocks in neuromorphic processors and spiking neural networks. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement