Abstract: The hardware acceleration of compute intensive applications has definite advantages, particularly in terms of energy and application latency. Heterogeneous programmable system-on-chip (SoCs) FPGA devices, which combine general purpose processors with reconfigurable fabrics, provide a compelling platform for IoT applications. However, FPGA devices are constrained due to significant design productivity issues and a lack of suitable hardware abstraction. For FPGAs to compete as general purpose computing platforms they must be better virtualized, as eliminating the need to work with platform-specific details would make them more accessible to application developers who are accustomed to software API abstractions and fast development cycles. In this paper, we discuss the role of overlay architectures for enabling general purpose FPGA application acceleration.

• Publication Date: 04-Feb-2018
• DOI: 10.15224/978-1-63248-144-3-12
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Abstract: To classify objects into different classes, feature plays a vital role. So identification of best features is a backbone of classification process. In text classification, features are simple words, having very large dimension so finding the most appropriate feature set is a big challenge. This paper includes analysis of some feature selection methods for multi class text classification and checks their results on different classifier for an email classification. We run our experiments on 20NewGroups and PU corpora datasets. Experiments are done on some well-known feature selection method like Term Selection, Document Frequency, Mutual Information, Odds Ratio, Chi square and etc. This paper concludes that Mutual Information and Chi square are most appropriate for text classification.

• Publication Date: 04-Feb-2018
• DOI: 10.15224/978-1-63248-144-3-26
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• Downloads: 0