





























Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
An in-depth exploration of reconfigurable computing systems, focusing on static and dynamic configuration. Topics include the piperench architecture and compiler, spyder and renco applications, and the concepts of static and dynamic reconfiguration. The document also discusses the objectives, performance, and hardware requirements of these systems.
Typology: Slides
1 / 37
This page cannot be seen from the preview
Don't miss anything!






























Static and Dynamic Configurable Systems
Piperench: A Reconfigurable Architecture and Compiler
A reconfigurable co-processor adaptable to given application in a transparent way The application is written with a high level language, compiler generates the best description for the hardware
First aim was transparent HW configuration The user just determines the operators in a high level language
Compiler then generates the corresponding code and does operations based on maximal parallelism.
A reconfigurable network computer for improved performance of the system RENCO adds the power of reconfiguration to the network computer. User can download not only his/her application but also the processor configuration
Composed of two parts:
RENCO’s μ-processor has high communication capabilities, integrated memory controller, and many SW tools are available.
Network computer requires a good OS for networking
For reconfigurable part many SW tools are availale(synthesizer, monitor for resource access & configuration loading, debugger etc) Java(Kaffe) is used for source code. HW libraries are built accordingly So like other reconfigurable systems SW is much harder than HW!
To handle changing and/or incomplete specifications
Based on the idea of applying the biological principle of natural evolution to artificial systems
A genetic algorithm is iterative procedure that starts with a random initial population
Firefly is based on cellular automata model consisting of an array of cells whose states are updated in every evolutionary step. A rule table, concerning the neighbour’s state, exists for the determination of the next state
After some steps iteration leads the cells to oscillate between all 0’s and all 1’s. Firefly inherits its name from this phenomenon
Firefly has 56 cells consisting of FPGA’s as the evolution platform. Firefly is a machine in which all the system evolution is carried out online, that is in hardware! Evolution rules and state of a cell are stored in D-flipflops.