Hardware Rough Set Processor Parallel Architecture in FPGA for Finding Core in Big Datasets

Abstract

This paper presents FPGA and softcore CPU based solution for large datasets parallel core calculation using rough set methods. Architectures shown in this paper have been tested on two real datasets running presented solutions inside FPGA unit. Tested datasets had 1 000 to 10 000 000 objects. The same operations were performed in software implementation. Obtained results show the big acceleration in computation time using hardware supporting core generation in comparison to pure software implementation.

Fixed-Point Arithmetic Unit with a Scaling Mechanism for FPGA-Based Embedded Systems

“…Thus, this method complies with the first of the categories presented in Reference [13] and is used in many practical solutions [11,16]. As mentioned before, this method provides the highest possible performance for various algorithms implemented on FPGAs [17], but the design process is tedious and lengthy. Additionally, it should be pointed out that in the case of some control algorithms their implementation based on this method does not make sense from an economic point of view.…”

Rough Sets Turn 40: From Information Systems to Intelligent Systems

“…We examined connections of rough sets with decision making [20], [21], logics [73], [76], probability [47], [72], statistical / machine learning [77], [105], data mining [19], [23], fuzzy sets [13], [48], formal concept analysis [26], [69], and other data / information / knowledge representation methodologies [31], [32]. We discussed some of rough set techniques aimed at attribute selection / reduction treated as a component of knowledge discovery processes [10], [17], with particular emphasis on computational scalability challenges [60], [82]. We paid special attention to rough set approaches to construction of interpretable (explainable by design) rule-based decision models [18], [48], [52], [97].…”

“…When thinking about the future it is also worth referring to the history. That reflected one more objective: acknowledging the 40th anniversary of rough sets [1], their founder [5], as well as some of relevant past and present events such as FedCSIS 2012 (rough set papers published exactly 10 years ago) [47], [48], [51], [53], [54], [57], [58], [59], [61], [64], [66], the PP-RAI 2022 rough set contest [69], [70], [72], [73], [77], [78], [81], [82], [83], [84], [89], [90], [91], [95], [96], [97], [98], [101], [105], [106] and celebration of the 30th CS&P -the event series whereby this paper's topics have been regularly addressed [36], [40], [44], [103], [116], [123].…”

“…Papers [81], [82] continue the topic of feature selection. The first paper refers to heuristic attribute evaluation measures and data discretization techniques analogous to those reported in [10], [19].…”