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Content Provider | IEEE Xplore Digital Library |
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Author | Al Faruque, M.A. Henkel, J. |
Copyright Year | 2008 |
Description | Author affiliation: CES - Dept. for Embedded Syst., Karlsruhe Univ., Karlsruhe (Al Faruque, M.A.; Henkel, J.) |
Abstract | We present a novel methodology for design space exploration using a two-steps scheme to optimize the number of virtual channel buffers (buffers take the premier share of the router in a NoC) used to implement logical channels multiplexed across the physical channel in a router output port for QoS supported on-chip communication. In the first step, the number of virtual channels is minimized during the mapping of tasks to the NoC at the design time of a system on chip (SoC)for which we use a swarm intelligence-based ant colony optimization (ACO) algorithm. In the second step, a probabilistic approach based on the traffic model of the application is used to further minimize the number of virtual channels. We achieve on average 90.2% reduction in the number of virtual channels compared to a fixed state- of-the-art (i.e. QNoC) allocation for the E3S embedded application benchmark suit. The reduction depends on the designer and the QoS parameter, and it is dependent on the specific application driven traffic model. We demonstrate our design space exploration by means of a complete robot application and also extend our exploration by evaluating the E3S embedded application benchmark suit. |
Starting Page | 1238 |
Ending Page | 1243 |
File Size | 360725 |
Page Count | 6 |
File Format | |
ISBN | 9783981080131 |
DOI | 10.1109/DATE.2008.4484848 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2008-03-10 |
Publisher Place | Germany |
Access Restriction | Subscribed |
Rights Holder | European Design Automation Association (EDAA) |
Subject Keyword | Space exploration Network-on-a-chip Traffic control Design methodology Design optimization Algorithm design and analysis System-on-a-chip Particle swarm optimization Ant colony optimization Orbital robotics |
Content Type | Text |
Resource Type | Article |
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