Design and Implementation of a Data-Driven Dynamical Reconfigurable Cell Array

The nature of dataflow computation demands the heavy flow of tokens amongst computation nodes.Traditional reduced instruction-set computer(RISC) processors are not suitable for such style computation. Devices that use long wire buses are not suitable for dataflow either. Reconfigurable computing dev...

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Published in:Shanghai jiao tong da xue xue bao 2017-08, Vol.22 (4), p.493-503
Main Author: 山蕊 李涛 蒋林 邓军勇 沈绪榜
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Buses
Clusters
Computation
Computer architecture
Computer Science
Data buses
Data management
Data processing
Data transfer (computers)
Electrical Engineering
Electronic devices
Engineering
Life Sciences
Materials Science
Nodes
Processors
Reconfiguration
RISC
Wire
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Summary:The nature of dataflow computation demands the heavy flow of tokens amongst computation nodes.Traditional reduced instruction-set computer(RISC) processors are not suitable for such style computation. Devices that use long wire buses are not suitable for dataflow either. Reconfigurable computing devices(RCDs)consist of data transfer wires and computing resources. With minor modifications, reconfigurable cells can be adopted to perform dataflow computation. A reconfigurable cell array(RCA) is presented in this paper and it is suitable for dataflow computation. This cell array has a dynamic reconfigurable storage model. The distinctive features of the architecture include dataflow reconfigurable cells and reconfigurable storage. Dataflow applications can be mapped easily and effectively onto the cells. Reconfigurable storage is mainly used to manage data access and transmission. Furthermore, computation and data management are separated. Meanwhile, dynamical reconfiguration is accomplished, when some clusters of cells work in configuration mode and other clusters work in computation mode. The dataflow graphs of some algorithms are mapped onto our architecture, and the performance results are compared with those of CPU and GPU.
ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-017-1862-0