Andres Upegui

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In this paper we present a platform for evolving spiking neural networks on FPGAs. Embedded intelligent applications require both high performance, so as to exhibit real-time behavior, and flexibility, to cope with the adaptivity requirements. While hardware solutions offer performance, and software solutions offer flexibility, reconfigurable computing(More)
Randomly connecting networks have proven to be universal computing machines. By interconnecting a set of nodes in a random way one can model very complicated non-linear dynamic systems. Although random Boolean networks (RBN) use Boolean functions as their basic component, there are not hardware implementations of such systems. The absence of implementations(More)
Purpose – This paper aims to present a novel modular robot that provides a flexible framework for exploring adaptive locomotion. Design/methodology/approach – A new modular robot is presented called YaMoR (for “Yet another Modular Robot”). Each YaMoR module contains an FPGA and a microcontroller supporting a wide range of control strategies and high(More)
Self-reconfigurable adaptive systems have the possibility of adapting their own hardware configuration. This feature provides enhanced performance and flexibility, reflected in computational cost reductions. Self-reconfigurable adaptation requires powerful optimization algorithms in order to search in a space of possible hardware configurations. If such(More)
The complexity exhibited by pervasive systems is constantly increasing. Customer electronics devices provide day to day a larger amount of functionalities. A common approach for guaranteeing high performance is to include specialized coprocessor units. However, these systems lack flexibility, since one must define, in advance, the coprocessor functionality.(More)
  • Andres Upegui
  • Intelligent Systems for Automated Learning and…
  • 2010
During the last few years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bit-string, providing high architectural flexibility, while guaranteeing high(More)
This paper introduces Perplexus, a European project that aims to develop a scalable hardware platform made of custom reconfigurable devices endowed with bio-inspired capabilities. This platform will enable the simulation of large-scale complex systems and the study of emergent complex behaviors in a virtually unbounded wireless network of computing modules.(More)
The ubichip is a bio-inspired reconfigurable circuit developed in the framework of the european project Perplexus. The ubichip offers special reconfigurability capabilities as self-replication and dynamic routing. This paper describes how to exploit the dynamic routing capabilities of the ubichip in order to implement plastic neural networks. We present an(More)
There is no systematic way to define the optimal topology of an artificial neural network for a given task. Heuristic methods, such as genetic algorithms, have been widely used to determine the number of neurons and the connectivity required for specific applications. However, artificial evolution uses to be highly time-consuming, making it unsuitable for(More)