Fat H-Tree: A cost-efficient tree-based on-chip network

Hiroki Matsutani, Michihiro Koibuchi, Yutaka Yamada, D. Frank Hsu, Hideharu Amano

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


The topological explorations of on-chip networks are important for efficiently using their enormous wire resources for low-latency and high-throughput communications using a modest silicon budget. In this paper, we propose a novel tree-based interconnection network called Fat H-Tree that meets these requirements. A Fat H-Tree provides a torus structure by combining two folded H-Tree networks and is an attractive alternative to tree-based networks such as the Fat Trees in a microarchitecture domain. We introduce its chip layout schemes based on a folding technique for 2D and 3D ICs. Three deadlock-free routing schemes are proposed for Fat H-Tree. We evaluate the performance of Fat H-Tree and other tree-based networks using real application traces. In addition, the network logic area, wire resource, and energy consumption of Fat H-Tree are compared with other topologies, based on a typical implementation of on-chip routers synthesized with a 90-nm standard cell library. The results show that 1) a Fat H-Tree outperforms a Fat Tree with two upward and four downward connections in terms of the throughput and average hop count, 2) a Fat H-Tree requires 19.8 percent-27.8 percent smaller network logic area than the Fat Tree, 3) a Fat H-Tree consumes slightly less energy than the Fat Tree does, and 4) a Fat H-Tree uses slightly more wire resources than the Fat Tree, but the current process technology can provide sufficient wire resources for implementing Fat-H-Tree-based on-chip networks.

Original languageEnglish
Pages (from-to)1126-1141
Number of pages16
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number8
Publication statusPublished - 2009


  • Interconnection networks
  • Network topology
  • On-chip networks
  • Routing algorithm
  • Tree

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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