Noticias


Empleo en el IMSE
Varias convocatorias abiertas

LEER MÁS

Entrevista Rosario Arjona
Biometría y su utilización en transacciones electrónicas

Entrevista a la investigadora del IMSE Rosario Arjona, acerca de la importancia de la privacidad y la utilización de técnicas biométricas en las transacciones electrónicas, en lugar de las tradicionales contraseñas de caracteres.

ESCUCHAR

Event Data Downscaling for Embedded Computer Vision

Presentación del artículo 'Event Data Downscaling for Embedded Computer Vision' en el Congreso VISAPP 2022.
Autores: Amélie Gruel, Jean Martinet, Teresa Serrano-Gotarredona y Bernabé Linares-Barranco.

Publicación en el blog La Cuadratura del Círculo
Publicación en el blog La Cuadratura del Círculo

Privacidad ante todo: pseudónimos confiables y post-cuánticos de personas y cosas.
Rosario Arjona López

LEER MÁS

Goit Project
Proyecto Europeo GOIT

El proyecto europeo GOIT, un de cuyos coordinadores es la investigadora del IMSE Piedad Brox, ha sido seleccionado para ser financiado en la convocatoria HORIZON-CL4-2021-DIGITAL-EMERGING-01 con una financiación global de 1.994.328 €

LEER MÁS

Visual Inference for IoT Systems
Libro 'Visual Inference for IoT Systems: A Practical Approach'

Publicado el libro 'Visual Inference for IoT Systems: A Practical Approach', de los investigadores Delia Velasco Montero, Jorge Fernández Berni y Ángel Rodríguez Vázquez.

LEER MÁS

EVENTOS Y NOTICIAS ANTERIORES

Formación en el IMSE


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

LEER MÁS

Publicaciones recientes


Fully Parallel Stochastic Computing Hardware Implementation of Convolutional Neural Networks for Edge Computing Applications
C. Frasser, P. Linares-Serrano, I.D. de los Rios, A. Moran, E.S. Skibinsky-Gitlin, J. Font-Rossello, V. Canals, M. Roca, T. Serrano-Gotarredona and J.L. Rossello
Journal Paper · IEEE Transactions on Neural Networks and Learning Systems, first online, 2022
IEEE    ISSN: 2162-237X
resumen      doi      

Edge artificial intelligence (AI) is receiving a tremendous amount of interest from the machine learning community due to the ever-increasing popularization of the Internet of Things (IoT). Unfortunately, the incorporation of AI characteristics to edge computing devices presents the drawbacks of being power and area hungry for typical deep learning techniques such as convolutional neural networks (CNNs). In this work, we propose a power-and-area efficient architecture based on the exploitation of the correlation phenomenon in stochastic computing (SC) systems. The proposed architecture solves the challenges that a CNN implementation with SC (SC-CNN) may present, such as the high resources used in binary-to-stochastic conversion, the inaccuracy produced by undesired correlation between signals, and the complexity of the stochastic maximum function implementation. To prove that our architecture meets the requirements of edge intelligence realization, we embed a fully parallel CNN in a single field-programmable gate array (FPGA) chip. The results obtained showed a better performance than traditional binary logic and other SC implementations. In addition, we performed a full VLSI synthesis of the proposed design, showing that it presents better overall characteristics than other recently published VLSI architectures.

The Influence of MPPT Algorithms in the Lifespan of the Capacitor Across the PV Array
A. Alcaide, R. Gomez-Merchan, E. Zafra, E.P. Martin, J.M. López-Rodriguez, J.I. Leon, S. Vazquez and L.G. Franquelo
Journal Paper · IEEE Access, vol. 10, pp 40945 - 40952, 2022
IEEE    ISSN: 2169-3536
resumen      doi      

PV systems efficiency highly depends on the MPPT strategy to be implemented in the PV converter. Many MPPT methods on the literature are focused on improving the steady state and transient system performance extracting the maximum energy from the sun. In this paper, the impact of the MPPT methods in the PV converter is analyzed focusing the study on the capacitor across the PV array lifespan. The obtained results demonstrate that the low frequency PV voltage oscillations that are present in many MPPT methods have a large negative impact on this capacitor lifespan. Experimental and simulation results are presented in order to show that advanced MPPT methods, which avoid these low frequency oscillations, achieve higher capacitor lifespan values compared with the values obtained by applying well-known MPPT methods such as the perturb and observe or incremental conductance strategies.

Event Data Downscaling for Embedded Computer Vision
A. Gruel, J. Martinet, T. Serrano-Gotarredona and B. Linares-Barranco
Conference · International Conference on Computer Vision Theory and Applications VISAPP 2022
resumen     

Event cameras (or silicon retinas) represent a new kind of sensor that measure pixel-wise changes in brightness and output asynchronous events accordingly. This novel technology allows for a sparse and energy-efficient recording and storage of visual information. While this type of data is sparse by definition, the event flow can be very high, up to 25M events per second, which requires significant processing resources to handle and therefore impedes embedded applications. Neuromorphic computer vision and event sensor based applications are receiving an increasing interest from the computer vision community (classification, detection, tracking, segmentation, etc.), especially for robotics or autonomous driving scenarios. Downscaling event data is an important feature in a system, especially if embedded, so as to be able to adjust the complexity of data to the available resources such as processing capability and power consumption. To the best of our knowledge, this works is the first attempt to formalize event data downscaling. In order to study the impact of spatial resolution downscaling, we compare several features of the resulting data, such as the total number of events, event density, information entropy, computation time and optical consistency as assessment criteria. Our code is available online at https://github.com/amygruel/EvVisu.

FeFETs for Phase Encoded Oscillatory based Computing
J. Núñez, M. Jiménez, B. Linares-Barranco and M.J. Avedillo
Conference · Design, Automation and Test in Europe DATE 2022
resumen     

Coupled nano-oscillators are attracting increasing interest because of their potential to perform computation efficiently, enabling new applications in computing and information processing. The potential of Ferroelectric Field-Effect Transistor (FeFET) for such applications has recently been recognized, which is a step towards the physical realization given their ease of cointegration with commercial CMOS technologies. This paper investigates the design of oscillators using FeFETs and their potential for oscillator-based computing in which information is encoded in phase. As applications, we present the operation of FeFET coupled oscillators systems for graph coloring and Max-Cut problems, including subharmonic injection mechanism to discretize the phase in the second one.

TODAS LAS PUBLICACIONES

Video institucional del IMSE


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.

LEER MÁS

Webs relacionadas con el IMSE


COMPARTIR