A fine-grained multicasting of configuration data for coarse-grained reconfigurable architectures

Takuya Kojima, Hideharu Amano

研究成果: Article

抜粋

A novel configuration data compression technique for coarse-grained reconfigurable architectures (CGRAs) is proposed. Reducing the size of configuration data of CGRAs shortens the reconfiguration time especially when the communication bandwidth between a CGRA and a host CPU is limited. In addition, it saves energy consumption of configuration cache and controller. The proposed technique is based on a multicast configuration technique called RoMultiC, which reduces the configuration time by multicasting the same data to multiple PEs (Processing Elements) with two bit-maps. Scheduling algorithms for an optimizing the order of multicasting have been proposed. However, the multicasting is possible only if each PE has completely the same configuration. In general, configuration data for CGRAs can be divided into some fields like machine code formats of general perpose CPUs. The proposed scheme confines a part of fields for multicasting so that the possibility of multicasting more PEs can be increased. This paper analyzes algorithms to find a configuration pattern which maximizes the number of multicasted PEs. We implemented the proposed scheme to CMA (Cool Mega Array), a straight forward CGRA as a case study. Experimental results show that the proposed method achieves 40.0% smaller configuration than a previous method for an image processing application at maximum. The exploration of the multicasted grain size reveals the effective grain size for each algorithm. Furthermore, since both a dynamic power consumption of the configuration controller and a configuration time are improved, it achieves 50.1% reduction of the energy consumption for the configuration with a negligible area overhead.

元の言語English
ページ(範囲)1247-1256
ページ数10
ジャーナルIEICE Transactions on Information and Systems
E102D
発行部数7
DOI
出版物ステータスPublished - 2019 1 1

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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