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Advanced Encryption Standard AESS) is widely used symmetric cryptographic algorithm due to its ease in implementation on hardware and software.
As such k-means is a well suited algorithm for implementation on hardware platform such as Field Programmable Gate Array (FPGA) but requires high computation time.
However, the use of these matrices for real-world applications is limited for several reasons: no fast matrix multiplication algorithm is available, huge memory requirements for large scale problems, difficult implementation on hardware, etc.
This paper aims at the proposition of novel architectures for radial basis function neural networks implementation on hardware with custom-precision floating-point operations for black-box system modeling.
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The implementation on target hardware requires the replacement of simulation inputs and outputs by real signals.
In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations.
Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article.
These look-up tables can be efficiently represented in a tree-structured architecture which allows the implementation on lowcost hardware.
Several heuristics have been introduced to make it suitable for implementation on parallel/distributed hardware.
Even Symbian has some QA, which means you won't get a bum implementation on janky hardware if Nokia has anything to say about it.
However, competitive time to solution not only depends on the choice of a reasonable method, but also on an efficient implementation on modern hardware.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com