arXiv subjects

Antonio Hurtado

Publications and source records attributed to Antonio Hurtado.

27 records · Page 2Linked to original sources

arXiv2022

GHz Rate Neuromorphic Photonic Spiking Neural Network with a Single Vertical-Cavity Surface-Emitting Laser (VCSEL)

Vertical-Cavity Surface-Emitting Lasers (VCSELs) are highly promising devices for the construction of neuromorphic photonic information processing systems, due to their numerous desirable properties such as low power consumption, high modulation speed, compactness, and ease of manufacturing. Of particular interest is the ability of VCSELs to exhibit neural-like spiking responses, much like biological neurons, but at ultrafast sub-nanosecond rates; thus offering great prospects for high-speed light-enabled neuromorphic (spike-based) processors. Recent works have shown the use the spiking dynamics in VCSELs for pattern recognition and image processing problems such as image data encoding and edge-feature detection. Additionally, VCSELs have also been used recently as nonlinear elements in photonic reservoir computing (RC) implementations, yielding excellent state of the art operation. This work introduces and experimentally demonstrates for the first time the new concept of a Ghz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The reported system effectively implements a photonic VCSEL-based spiking reservoir computer, and demonstrates its successful application to a complex nonlinear classification task. Importantly, the proposed system benefits from a highly hardware-friendly, inexpensive realization (built with a single VCSEL and off-the-shelf fibre-optic components), for high-speed (GHz-rate inputs) and low-power (sub-mW optical input power) photonic operation. These results open new pathways towards future neuromorphic photonic spike-based information processing systems based upon VCSELs (or other laser types) for novel ultrafast machine learning and AI hardware.

cs.ET↗

arXiv2021

Photonic neuromorphic computing using vertical cavity semiconductor lasers

Photonic realizations of neural network computing hardware are a promising approach to enable future scalability of neuromorphic computing. In this review we provide an overview on vertical-cavity surface-emitting lasers (VCSELs) and how these high-performance electro-optical components either implement or are combined with additional photonic hardware to demonstrate points (i-iii). In the neurmorphic photonics' context, VCSELs are of exceptional interest as they are compatible with CMOS fabrication, readily achieve 30\% wall-plug efficiency and >30~GHz modulation bandwidth and hence are highly energy efficient and ultra-fast. Crucially, they react highly nonlinear to optical injection as well as to electrical modulation, making them highly suitable as all-optical as well as electro-optical photonic neurons. Their optical cavities are wavelength-limited, and standard semiconductor growth and lithography enables non-classical cavity configurations and geometries. This enables excitable VCSELs (i.e. spiking VCSELs) to finely control their temporal and spatial coherence, to unlock Terahertz bandwidths through spin-flip effects, and even to leverage cavity quantum electrodynamics to further boost their efficiency. Finally, as VCSEL arrays they are compatible with standard 2D photonic integration, but their emission vertical to the substrate makes them ideally suited for scalable integrated networks leveraging 3D photonic waveguides. Here, we discuss the implementation of spatially as well as temporally multiplexed VCSEL neural networks and reservoirs, computation on the basis of excitable VCSELs as photonic spiking neurons, as well as concepts and advances in the fabrication of VCSELs and microlasers.

cs.ET↗

arXiv2021

Resonant tunnelling diode nano-optoelectronic spiking nodes for neuromorphic information processing

In this work, we introduce an optoelectronic spiking artificial neuron capable of operating at ultrafast rates ($\approx$ 100 ps/optical spike) and with low energy consumption ($<$ pJ/spike). The proposed system combines an excitable resonant tunnelling 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 Gbps 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 neuromorphic hardware.

physics.app-ph↗

arXiv2021

Ultrafast Neuromorphic Photonic Image Processing with a VCSEL Neuron

The ever-increasing demand for Artificial Intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon Vertical Cavity Surface Emitting Lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potentials of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.

cs.ET↗

arXiv2020

All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes

All-optical binary convolution with a photonic spiking vertical-cavity surface-emitting laser (VCSEL) neuron is proposed and demonstrated experimentally for the first time. Optical inputs, extracted from digital images and temporally encoded using rectangular pulses, are injected in the VCSEL neuron which delivers the convolution result in the number of fast (<100 ps long) spikes fired. Experimental and numerical results show that binary convolution is achieved successfully with a single spiking VCSEL neuron and that all-optical binary convolution can be used to calculate image gradient magnitudes to detect edge features and separate vertical and horizontal components in source images. We also show that this all-optical spiking binary convolution system is robust to noise and can operate with high-resolution images. Additionally, the proposed system offers important advantages such as ultrafast speed, high energy efficiency and simple hardware implementation, highlighting the potentials of spiking photonic VCSEL neurons for high-speed neuromorphic image processing systems and future photonic spiking convolutional neural networks.

physics.optics↗

arXiv2020

Convolutional Image Edge Detection Using Ultrafast Photonic Spiking VCSEL Neurons

We report experimentally and in theory on the detection of edge information in digital images using ultrafast spiking optical artificial neurons towards convolutional neural networks (CNNs). In tandem with traditional convolution techniques, a photonic neuron model based on a Vertical-Cavity Surface Emitting Laser (VCSEL) is implemented experimentally to threshold and activate fast spiking responses upon the detection of target edge features in digital images. Edges of different directionalities are detected using individual kernel operators and complete image edge detection is achieved using gradient magnitude. Importantly, the neuromorphic (brain-like) image edge detection system of this work uses commercially sourced VCSELs exhibiting spiking responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-center technologies. These results therefore have exciting prospects for ultrafast photonic implementations of neural networks towards computer vision and decision making systems for future artificial intelligence applications.

eess.IV↗

arXiv2020

Characterisation, Selection and Micro-Assembly of Nanowire Laser Systems

Semiconductor nanowire (NW) lasers are a promising technology for the realisation of coherent optical sources with extremely small footprint. To fully realize their potential as building blocks in on-chip photonic systems, scalable methods are required for dealing with large populations of inhomogeneous devices that are typically randomly distributed on host substrates. In this work two complementary, high-throughput techniques are combined: the characterisation of nanowire laser populations using automated optical microscopy, and a high accuracy transfer printing process with automatic device spatial registration and transfer. In this work a population of NW lasers is characterised, binned by threshold energy density and subsequently printed in arrays onto a secondary substrate. Statistical analysis of the transferred and control devices show that the transfer process does not incur measurable laser damage and the threshold binning can be maintained. Analysis is provided on the threshold and mode spectra of the device populations to investigate the potential for using NW lasers for integrated systems fabrication.

physics.app-ph↗

arXiv2019

Integration of Text-maps in Convolutional Neural Networks for Region Detection among Different Textual Categories

In this work, we propose a new technique that combines appearance and text in a Convolutional Neural Network (CNN), with the aim of detecting regions of different textual categories. We define a novel visual representation of the semantic meaning of text that allows a seamless integration in a standard CNN architecture. This representation, referred to as text-map, is integrated with the actual image to provide a much richer input to the network. Text-maps are colored with different intensities depending on the relevance of the words recognized over the image. Concretely, these words are previously extracted using Optical Character Recognition (OCR) and they are colored according to the probability of belonging to a textual category of interest. In this sense, this solution is especially relevant in the context of item coding for supermarket products, where different types of textual categories must be identified, such as ingredients or nutritional facts. We evaluated our solution in the proprietary item coding dataset of Nielsen Brandbank, which contains more than 10,000 images for train and 2,000 images for test. The reported results demonstrate that our approach focused on visual and textual data outperforms state-of-the-art algorithms only based on appearance, such as standard Faster R-CNN. These enhancements are reflected in precision and recall, which are improved in 42 and 33 points respectively.

cs.CV↗

arXiv2015

Controllable spiking patterns in long-wavelength VCSELs for neuromorphic photonics systems

Multiple controllable spiking patterns are obtained in a 1310 nm Vertical Cavity Surface Emitting Laser (VCSEL) in response to induced perturbations and for two different cases of polarized optical injection, namely parallel and orthogonal. Achievement of reproducible spiking responses in VCSELs operating at the telecom wavelengths offers great promise for future uses of these devices in ultrafast neuromorphic photonic systems for non-traditional computing applications.

physics.optics↗