Abstract
Rough set theory, developed by Z. Pawlak, is a powerful soft computing tool for extracting meaningful patterns from vague, imprecise, inconsistent and large chunk of data. It classifies the given knowledge base approximately into suitable decision classes by removing irrelevant and redundant data using attribute reduction algorithm. Conventional Rough set information processing like discovering data dependencies, data reduction, and approximate set classification involves the use of software running on general purpose processor. Since last decade, researchers have started exploring the feasibility of these algorithms on FPGA. The algorithms implemented on a conventional processor using any standard software routine offers high flexibility but the performance deteriorates while handling larger real time databases. With the tremendous growth in FPGA, a new area of research has boomed up. FPGA offers a promising solution in terms of speed, power and cost and researchers have proved the benefits of mapping rough set algorithms on FGPA. In this paper, a survey on hardware implementation of rough set algorithms by various researchers is elaborated.
Highlights
Rough set theory(RST), by Zdzisław Pawlak, is a powerful mathematical tool, for discovering data dependencies by reducing the number of attributes contained in a data set using the data alone, without requiring any further additional information like degree of membership, probability etc. as required in fuzzy or in probability theory[1]
A reduct is any minimal subset of condition features, which discerns all pairs with different decision values and is complete if the deletion of any attribute of a reduct will make at least one pair of objects with different decision attribute values indiscernible
In order to overcome the problems posed by power and instruction-level parallelism (ILP) wall, the computer industry shifted from single core processors to multiple parallel processing units
Summary
Rough set theory(RST), by Zdzisław Pawlak, is a powerful mathematical tool, for discovering data dependencies by reducing the number of attributes contained in a data set using the data alone, without requiring any further additional information like degree of membership, probability etc. as required in fuzzy or in probability theory[1]. The rough set approach is easy to understand, offers straightforward interpretation of obtained results, most of its algorithms are suited for parallel processing. It is considered as one of the first non-statistical approach in data analysis [2]. There has been a growing interest amongst researchers in developing a dedicated hardware for RST using FPGAs. The advantage of using a dedicated hardware is huge acceleration in terms of speed as they relieve main processor from the computational overheads. The advantage of using a dedicated hardware is huge acceleration in terms of speed as they relieve main processor from the computational overheads There are several such accelerators already available commercially in markets like Graphics Processing Units (GPUs), Digital Signal Processor (DSP), Fuzzy Processor.
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