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ERC group
MSCA Group
Scientific Supervisor
Alberto A. Del Barrio
Contact email
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Research group
Artecs
Department
Computer Architecture and Automation
Faculty / Institute
Faculty of Computer Science and Engineering
Group description
The research activity of the Group of Architecture and Technology of Computing Systems (ArTeCS) of the Complutense University of Madrid is focused on the conception and construction of digital information processing systems, and its efficient application regarding performance, energy consumption, and cost. Within this broad area, the group pays special attention to high-performance computing, processor & memory hierarchy design and embedded systems.
The research activity of the ArTeCS group covers a wide range of subjects related with high performance computing, computer architecture and system design. Furthermore, the ArTeCS group is one of the groups in UCM graded as “Excellent” by an evaluation commissioned by the State Research Agency.
Two of the most relevant researching lines of the group is the use of Specialized Arithmetic and Approximate Computing as well as Multimedia, both critical for performing visual recognition in real time. Furthermore, recently in collaboration with the University of California at Irvine (UCI), some of the researchers have started a promising line regarding Deep Neural Networks and logarithmic Arithmetic to improve the performance and power consumption of accelerators leveraging this technology.
Some of the resulting code is publicly available at:
https://github.com/albertodbg/log-arithmetic
The research activity of the ArTeCS group covers a wide range of subjects related with high performance computing, computer architecture and system design. Furthermore, the ArTeCS group is one of the groups in UCM graded as “Excellent” by an evaluation commissioned by the State Research Agency.
Two of the most relevant researching lines of the group is the use of Specialized Arithmetic and Approximate Computing as well as Multimedia, both critical for performing visual recognition in real time. Furthermore, recently in collaboration with the University of California at Irvine (UCI), some of the researchers have started a promising line regarding Deep Neural Networks and logarithmic Arithmetic to improve the performance and power consumption of accelerators leveraging this technology.
Some of the resulting code is publicly available at:
https://github.com/albertodbg/log-arithmetic
Research group website
Research topic
The recent breakthroughs in Machine Learning applications, especially in Deep Neural Networks (DNNs), have caused significant progress in image classification and speech recognition applications. Autonomous driving is arguably one of the most important final applications making use of DNNs. Thanks to the use of these networks a vehicle can interpret what is happening around, the traffic signs and even the objects and people that are in the range of vision of the vehicle.
Nevertheless, Computer Vision is one of the toughest problems in Artificial Intelligence. Perceiving the surroundings accurately, quickly and energy efficiently is one of the most essential and challenging tasks for autonomous systems such as self-driving cars. Besides this real-time recognition requirement, recent works have proved that the output of DNNs can easily be fooled by adding relatively small perturbations to the input vector. And, what is more, with the irruption of Generative Adversarial Networks (GAN) this process of mimicking reality can even be automated. For instance, in a different scenario, Christie’s sold a portrait for $432,000 that had been generated by a GAN, based on open-source code written by Robbie Barrat of Stanford. Although not related with self-driving, this case gives an idea about the importance of reinforcing security in DNNs.
The proposal would then be focused on devising a real-time platform able to deal with the aforementioned issues and, regarding DNN security, being able to detect and counteract these threats.
Nevertheless, Computer Vision is one of the toughest problems in Artificial Intelligence. Perceiving the surroundings accurately, quickly and energy efficiently is one of the most essential and challenging tasks for autonomous systems such as self-driving cars. Besides this real-time recognition requirement, recent works have proved that the output of DNNs can easily be fooled by adding relatively small perturbations to the input vector. And, what is more, with the irruption of Generative Adversarial Networks (GAN) this process of mimicking reality can even be automated. For instance, in a different scenario, Christie’s sold a portrait for $432,000 that had been generated by a GAN, based on open-source code written by Robbie Barrat of Stanford. Although not related with self-driving, this case gives an idea about the importance of reinforcing security in DNNs.
The proposal would then be focused on devising a real-time platform able to deal with the aforementioned issues and, regarding DNN security, being able to detect and counteract these threats.
Research area
Information Science and Engineering (ENG), Mathematics (MAT), Physics (PHY)
Candidatures: requirements
* A motivation letter identifying research synergies (max 1 page)
* Your CV (including a list of publications) limited to a maximum of 4 pages
* Short summary of your proposed project idea (max 1 page)
* Your CV (including a list of publications) limited to a maximum of 4 pages
* Short summary of your proposed project idea (max 1 page)
Candidatures: deadline
2020-06-15
Address
C/ Profesor José García Santesmases, 9; Ciudad Universitaria; 28040 - MADRID
