• Journal of KIISE:Computing Practices and Letters
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• v.13 no.4
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• pp.231-239
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• 2007

This paper presents the trade-off relationship between area and performance in the hardware design space exploration for the Korean national standard 128-bit block cipher algorithm SEED. In this paper, we compare the following four hardware design types of SEED algorithm : (1) Design 1 that is 16 round fully pipelining approach, (2) Design 2 that is a one round looping approach, (3) Design 3 that is a G function sharing and looping approach, and (4) Design 4 that is one round with internal 3 stage pipelining approach. The Design 1, Design 2, and Design 3 are the existing design approaches while the Design 4 is the newly proposed design in this paper. Our new design employs the pipeline between three G-functions and adders consisting of a F function, which results in the less area requirement than Design 2 and achieves the higher performance than Design 2 and Design 3 due to pipelining and module sharing techniques. We design and implement all the comparing four approaches with real hardware targeting FPGA for the purpose of exact performance and area analysis. The experimental results show that Design 4 has the highest performance except Design 1 which pursues very aggressive parallelism at the expanse of area. Our proposed design (Design 4) shows the best throughput/area ratio among all the alternatives by 2.8 times. Therefore, our new design for SEED is the most efficient design comparing with the existing designs.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.