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Timestamp:
Feb 6, 2011, 2:29:09 PM (14 years ago)
Author:
coach
Message:

1ere Pre-release

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  • anr/section-etat-de-art.tex

    r339 r356  
    2525By adapting architecture to the software, % (the opposite is done in the others families)
    2626FPGAs architectures enable better performance
    27 (typically between x10 and x100 accelerations)
     27(typically an acceleration factor between 10 and 100)
    2828while using smaller size and less energy (and heat).
    2929However, using FPGAs presents significant challenges~\cite{hpc06a}.
     
    128128\item The parallelism is extracted from the initial specification.
    129129To get more parallelism or to reduce the amount of required memory in the SoC, the user
    130 must re-write the algorithmic specification while there are techniques such as polyedric
     130must re-write the algorithmic specification while there are techniques such as polyhedral
    131131transformations to increase the intrinsic parallelism,
    132132\item While they support limited loop transformations like loop unrolling and loop
     
    213213Since hardware is inherently parallel, finding parallelism in sequential
    214214programs in an important prerequisite for HLS. The large FPGA chips of
    215 today can accomodate much more parallelism than is available in basic blocks.
     215today can accommodate much more parallelism than is available in basic blocks.
    216216The polyhedral model is the ideal tool for finding more parallelism in
    217217loops.
     
    219219As a side effect, it has been observed that the polyhedral model is a useful
    220220tool for many other optimization, like memory reduction and locality
    221 improvement. Itshould be noted
     221improvement. It should be noted
    222222that the polyhedral model \emph{stricto sensu} applies only to
    223223very regular programs. Its extension to more general programs is
     
    251251further releases.
    252252\parlf
    253 In IP-XACT the flow automation and data constistency is ensured by generators, which
     253In IP-XACT the flow automation and data consistency is ensured by generators, which
    254254are program modules that process IP-XACT XML data into something useful
    255255for the design. They are key portable mechanism for encapsulating specialist design
    256256knowledge and enable designers to deploy specialist knowledge in their design. It is
    257257always possible to create generators in order to link several design or analysis tools
    258 around a centric representation of metadata in IP-XACT. This kind of XML schema for
    259 metadata management is a good solution for the federation of heterogeneous design domains
     258around a centric representation of meta-data in IP-XACT. This kind of XML schema for
     259meta-data management is a good solution for the federation of heterogeneous design domains
    260260(models, tools, languages, methodologies, etc.).
    261261
    262 %\subsubsection{High Performance Computing}
    263 %Accelerating high-performance computing (HPC) applications with field-programmable
    264 %gate arrays (FPGAs) can potentially improve performance.
    265 %However, using FPGAs presents significant challenges~\cite{hpc06a}.
    266 %First, the operating frequency of an FPGA is low compared to a high-end microprocessor.
    267 %Second, based on Amdahl law,  HPC/FPGA application performance is unusually sensitive
    268 %to the implementation quality~\cite{hpc06b}.
    269 %Finally, High-performance computing programmers are a highly sophisticated but scarce
    270 %resource. Such programmers are expected to readily use new technology but lack the time
    271 %to learn a completely new skill such as logic design~\cite{hpc07a} .
    272 %\\
    273 %HPC/FPGA hardware is only now emerging and in early commercial stages,
    274 %but these techniques have not yet caught up.
    275 %Thus, much effort is required to develop design tools that translate high level
    276 %language programs to FPGA configurations.
    277 
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