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Title: Performance estimation of FPGA modules for modular design methodology using artificial neural network
Authors: Herath, Kalindu
Prakash, Alok
Srikanthan, Thambipillai
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Herath, K., Prakash, A., & Srikanthan, T. (2018). Performance estimation of FPGA modules for modular design methodology using artificial neural network. International Symposium on Applied Reconfigurable Computing, 105-118. doi:10.1007/978-3-319-78890-6_9
Abstract: Modern FPGAs consist of millions of logic resources allowing hardware designers to map increasingly large designs. However, the design productivity of mapping large designs is greatly affected by the long runtime of FPGA CAD flow. To mitigate it, modular design methodology has been introduced in the past that allows designers to partition large designs into smaller modules and compile & test the modules individually before assembling them together to complete the compilation process. Automated decision making on placing these modules on FPGA, however, is a slow and tedious process that requires large database of pre-compiled modules, which are compiled on a large number of placement positions. To accelerate this placement process during modular designing, in this paper we propose an ANN based performance estimation technique that can rapidly suggest the best shape and location for a given module. Experimental results on legacy as well as state-of-the-art FPGA devices show that the proposed technique can accurately estimate the Fmax of modules with an average error of less than 4%.
ISBN: 9783319788890
DOI: 10.1007/978-3-319-78890-6_9
Rights: This is a post-peer-review, pre-copyedit version of an article published in International Symposium on Applied Reconfigurable Computing. The final authenticated version is available online at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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