A dataflow IR for memory efficient RIPL compilation to FPGAs

STEWART, Robert, MICHAELSON, Greg, BHOWMIK, Deepayan, GARCIA, Paulo and WALLACE, Andy (2016). A dataflow IR for memory efficient RIPL compilation to FPGAs. In: CARRETERO, Jesus, GARCIA-BLAS, Javier, GERGEL, Victor, VOEVODIN, Vladimir, MEYEROV, Iosif, RICO-GALLEGO, Juan A., DIAZ-MARTIN, Juan C., ALONSO, Pedro, DURILLO, Juan, GARCIA SANCHEZ, Jose Daniel, LASTOVETSKY, Alexey L., MAROZZO, Fabrizio, LIU, Qin, BHUIYAN, Zakirul Alam, FURLINGER, Karl, WEIDENDORFER, Josef and GARCIA, Jose, (eds.) Algorithms and architectures for parallel processing : ICA3PP 2016 Collocated Workshops: SCDT, TAPEMS, BigTrust, UCER, DLMCS, Granada, Spain, December 14-16, 2016, Proceedings. Lecture Notes in Computer Science (10049). Springer, 174-188.

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Bhowmik - Dataflow IR for memory efficient RIPL (AM).pdf - Accepted Version
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Official URL: http://link.springer.com/chapter/10.1007%2F978-3-3...
Link to published version:: 10.1007/978-3-319-49956-7_14

Abstract

Field programmable gate arrays (FPGAs) are fundamentally different to fixed processors architectures because their memory hierarchies can be tailored to the needs of an algorithm. FPGA compilers for high level languages are not hindered by fixed memory hierarchies. The constraint when compiling to FPGAs is the availability of resources. In this paper we describe how the dataflow intermediary of our declarative FPGA image processing DSL called RIPL (Rathlin Image Processing Language) enables us to constrain memory. We use five benchmarks to demonstrate that memory use with RIPL is comparable to the Vivado HLS OpenCV library without the need for language pragmas to guide hardware synthesis. The benchmarks also show that RIPL is more expressive than the Darkroom FPGA image processing language.

Item Type: Book Section
Additional Information: Series ISSN - 0302-9743 Paper original presented at the International Workshop on Data Locality in Modern Computing Systems (DLMCS 2016)
Uncontrolled Keywords: Domain specific languages – FPGAs – Data locality
Departments: Arts, Computing, Engineering and Sciences > Computing
Identification Number: 10.1007/978-3-319-49956-7_14
Depositing User: Deepayan Bhowmik
Date Deposited: 18 Jan 2017 12:08
Last Modified: 21 Jan 2017 20:34
URI: http://shura.shu.ac.uk/id/eprint/14205

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