scholarly journals Toward a Dynamically Reconfigurable Computing and Communication System for Small Spacecraft

2003 ◽  
Author(s):  
Muli Kifle ◽  
Monty Andro ◽  
Quang Tran ◽  
Gene Fujikawa ◽  
Pong Chu
Keyword(s):  
Communication System ◽  
Reconfigurable Computing ◽  
Small Spacecraft ◽  
Dynamically Reconfigurable

A Heterogeneous Digital Signal Processor for Dynamically Reconfigurable Computing

2010 ◽  
Vol 45 (8) ◽  
pp. 1615-1626 ◽  
Author(s):  
Davide Rossi ◽  
Fabio Campi ◽  
Simone Spolzino ◽  
Stefano Pucillo ◽  
Roberto Guerrieri
Keyword(s):  
Digital Signal Processor ◽  
Reconfigurable Computing ◽  
Digital Signal ◽  
Dynamically Reconfigurable ◽  
Signal Processor

Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures

2010 ◽  
pp. 1-22 ◽  
Author(s):  
Andres Upegui
Keyword(s):  
Reconfigurable Computing ◽  
High Performance ◽  
Environmental Changes ◽  
Boolean Networks ◽  
Research Field ◽  
Evolvable Hardware ◽  
Neuron Models ◽  
Random Boolean Networks ◽  
Dynamically Reconfigurable ◽  
Spiking Neuron Models

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 performance. On the other hand, we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse approaches like evolvable hardware, neural hardware, and fuzzy hardware. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in the early stages and they are not well supported by FPGA vendors, thus making their inclusion difficult in existing bio-inspired systems. This chapter presents a set of methodologies and architectures for exploiting the reconfigurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures are neural networks, spiking neuron models, fuzzy systems, cellular automata and Random Boolean Networks.

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