Noticias


World Top Scientists 2023
World's Top 2% Scientists 2023

Dos investigadores del Instituto de Microelectrónica de Sevilla (IMSE-CNM), Teresa Serrano Gotarredona y Bernabé Linares Barranco, mantienen su inclusión en la nueva edición del ranking 'World's Top 2% Scientists' de la Universidad de Stanford (California).
21 Noviembre 2023

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Ojo Electrónico Sistema Visión
El ojo electrónico que imita la visión humana

El Instituto de Microelectrónica de la capital andaluza (IMSE), dependiente del Consejo Superior de Investigaciones Científicas (CSIC) y la Universidad de Sevilla, se ha centrado en el sistema que hace posible la visión.
20 Noviembre 2023

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Nanochip desarrollado en el IMSE
Investigadores españoles desarrollan un nanochip para proteger los dispositivos electrónicos frente a ciberataques

El chip, obtenido por investigadores del IMSE (CSIC-Universidad de Sevilla), permite generar una clave digital única del dispositivo que puede usarse para generar contraseñas criptográficas efímeras de alta seguridad. El chip es resultado del proyecto SPIRS.
9 Octubre 2023

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Congreso ESSCIRC ESSDERC 2023
La Noche Europea de l@s Investigador@s 2023

Te invitamos a descubrir el lado más humano de la investigación a través de un contacto directo con los propios expertos y expertas. Es La Noche Europea de l@s Investigador@s, que celebramos este año el 29 de septiembre por duodécimo año consecutivo y al mismo tiempo que en casi 400 ciudades europeas.
25 Septiembre 2023

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Comienzo del Proyecto QUBIP
Comienzo del Proyecto QUBIP

La reunión de comienzo del proyecto QUBIP se celebró los día 12-13 de septiembre en Turín (Italia). QUBIP es un proyecto financiado por la Comisión Europea (programa Horizonte Europa - clúster 3 "Increased cybersecurity") que es coordinado por la Fundación LINKS. En QUBIP, el IMSE participa en el piloto que explora la transición hacia criptografía post-cuántica en el piloto de IoT bajo la coordinación de Dr. Piedad Brox.
25 Septiembre 2023

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Congreso ESSCIRC ESSDERC 2023
Edición Histórica del Principal Congreso de Microelectrónica en Europa

Los pasados días 11 al 14 de septiembre se ha celebrado en Lisboa el congreso ESSCIRC/ESSDERC. que ha sido organizado conjuntamente por investigadores de la UNINOVA Institute & NOVA School of Science (Lisboa), ST Microelectronics y del Instituto de Microelectrónica de Sevilla, centro mixto de la Universidad de Sevilla y del Consejo Superior de Investigaciones Científicas.
21 Septiembre 2023

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EVENTOS Y NOTICIAS ANTERIORES

Nueva Directora del IMSE-CNM


La investigadora del IMSE Teresa Serrano Gotarredona ha sido nombrada nueva Directora del Instituto de Microelectrónica de Sevilla.

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Formación en el IMSE


- Doctorado
- Máster
- Grados
- Trabajos Fin de Grado
- Prácticas en Empresa

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Publicaciones recientes


Experimental Demonstration of Coupled Differential Oscillator Networks for Versatile Applications
M. Jiménez, J. Núñez, J. Shamsi, B. Linares-Barranco and M.J. Avedillo
Journal Paper · Frontiers in Neuroscience, Neuromorphic Engineering, vol. 17, 2023
FRONTIERS    ISSN: 1662-453X
resumen      doi      

Oscillatory Neural Networks (ONNs) exhibit a high potential for energy-efficient computing. In ONNs, neurons are implemented with oscillators and synapses with resistive and/or capacitive coupling between pairs of oscillators. Computing is carried out on the basis of the rich, complex, nonlinear synchronization dynamics of a system of coupled oscillators. The exploited synchronization phenomena in ONNs are an example of fully parallel collective computing.A fast system´s convergence to stable states, which correspond to the desired processed information, enables an energy-efficient solution if small area and low-power oscillators are used, specifically, when they are built on the basis of the hysteresis exhibited by phase-transition materials such as VO2. In recent years, there have been numerous studies on ONNs using VO2. Most of them report simulation results. Although in some cases experimental results are also shown, they don´t implement the design techniques that other works on electrical simulations report that allow to improve the behavior of the ONNs.Experimental validation of these approaches is necessary. Therefore, in this work, we describe an ONN realized in a commercial CMOS technology in which the oscillators are built using a circuit that we have developed to emulate the VO2 device. The purpose is to be able to study in depth the synchronization dynamics of relaxation oscillators similar to those that can be performed with VO2 devices. The fabricated circuit is very flexible. It allows programming the synapses to implement different ONNs, calibrating the frequency of the oscillators or controlling their initialization. It uses differential oscillators and resistive synapses equivalent to the use of memristors. In this article, the designed and fabricated circuit is described in detail and experimental results are shown. Specifically, its satisfactory operation as an associative memory is demonstrated. The experiments carried out allow us to conclude that the ONN must be operated according to the type of computational task to be solved, and guidelines are extracted in this regard.

Learning Algorithms for Oscillatory Neural Networks as Associative Memory for Pattern Recognition
M. Jiménez, M.J. Avedillo, B. Linares-Barranco and J. Núñez
Journal Paper · Frontiers in Neuroscience, Neuromorphic Engineering, vol. 17, 2023
FRONTIERS    ISSN: 1662-453X
resumen      doi      

Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials like VO2 constitute an energy-efficient, massively parallel, brain-inspired, in-memory computing approach. The encoding of information in the phase pattern of frequency-locked, weakly coupled oscillators makes it possible to exploit their rich nonlinear dynamics and their synchronization phenomena for computing. A single fully connected ONN layer can implement an auto-associative memory comparable to that of a Hopfield network, hence Hebbian learning rule is the most widely adopted method for configuring ONNs for such applications, despite its well-known limitations. An extensive amount of literature is available about learning in Hopfield networks, with information regarding many different learning algorithms that perform better than the Hebbian rule. However, not all of these algorithms are useful for ONN training due to the constraints imposed by their physical implementation. This paper evaluates different learning methods with respect to their suitability for ONNs. It proposes a new approach, which is compared against previous works. The proposed method has been shown to produce competitive results in terms of pattern recognition accuracy with reduced precision in synaptic weights, and to be suitable for online learning.

Exploitation of Subharmonic Injection Locking for Solving Combinatorial Optimization Problems with Coupled Oscillators using VO2 based devices
J. Núñez, M.J. Avedillo and M. Jiménez
Conference · International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design SMACD 2023
resumen     

Abstract not available

Energy-efficient Brain-inspired Oscillatory Neural Networks using Phase-Transition Material
M. Jiménez, B. Linares-Barranco, M.J. Avedillo and J. Núñez
Conference · Workshop on Deep Learning meets Neuromorphic Hardware. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML PKDD 2023
resumen     

Oscillatory Neural Network (ONN) is a promising neuromorphic computing approach which uses networks of frequency-locked coupled oscillators, and their inherent parallel synchronization to compute. Also, ONN can be im-plemented using phase-transition materials using nano-scale area, low voltage amplitude and reduced power consumption, being an efficient way to im-plement oscillator-based computing. In state-of-theart, ONN is built with a fully-connected architecture, with coupling configured depending on the tar-get application. Its most widespread use has been as associative memory, but recently it is gathering interest as a solver for non-deterministic polynomial time problem (NP-hard). This is performed on the basis of encoding the NP-problem in the Ising model, so ONN operates as an Ising machine. ONN state naturally evolves to minimum points in the Hamiltonian energy function re-sorting to its rich non-lineal dynamics, supposing a promising paradigm of fast, low-power, parallel computation.

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Qué hacemos en el IMSE


El área de especialización del Instituto es el diseño de circuitos integrados analógicos y de señal mixta en tecnología CMOS, así como su uso en diferentes contextos de aplicación tales como dispositivos biomédicos, comunicaciones inalámbricas, conversión de datos, sensores de visión inteligentes, ciberseguridad, computación neuromórfica y tecnología espacial.

La plantilla del IMSE-CNM está formada por unas cien personas, entre personal científico y de apoyo, que participan en el avance del conocimiento, la generación de diseños de alto nivel científico-técnico y la transferencia de tecnología.

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