23 research outputs found

    A Study on the Characteristics of Cash flow for Korean Shipping Companies Listed on the Stock Market

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    Shipping industry is very different from other industries in operating, investing and financing activities due to the characteristics of the industry. However, despite it has been carrying out many studies of cash flow for other industries, there have been nearly performed a study of cash flow for korean shipping companies. Accordingly, the purpose of this study is to check the characteristics of cash flow for korean shipping companies listed on stock market, and to examine the usefulness of cash flow information against corporate valuation index. To accomplish the purpose of this study, Firstly, the characteristics of korean shipping companies listed on the stock market are checked by comparing cash flow statement and cash flow ratio with the average of manufacturing businesses. Secondly, the usefulness of cash flow information is examined by analyzing spearman's rank correlation coefficient on classical financial ratio with cash flow ratio, and both classical financial ratio group and cash flow ratio group with corporate valuation index(EVA, EV). The results of this study can be summarized as followsFirstly, it was showed that, in cash flow statement standardized by total asset, cash flow from operating activities(CFO) was higher, the cash outflow from investing activities(COFI) was lower, and cash outflow from financial activities(COFF) was higher than the average of manufacturing businesses, and in cash flow ratio, profitability was more variable, long-term debt repayment capacity was lower, and short-term debt repayment capacity was higher than the average of manufacturing businesses. it is concluded that korean shipping companies listed on the stock market have been superior to manufacturing businesses in generating CFO. But the companies have taken negative policy in investing and financing activities. Secondly, it was established that the classical financial ratio only about operating income was significantly correlated with that of cash flow ratio. It is concluded that the information of classical financial ratio is not similar to the information of cash flow ratio. Thirdly, it was showed that classical financial ratio group was more significant correlated with EVA(economic value added) than cash flow ratio group, but two groups had similar extent of correlation with EV(enterprise value). It is concluded that cash flow ratio is not superior to classical financial ratio in the corporate valuation.์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 ์ œ2์ ˆ ์—ฐ๊ตฌ๋‚ด์šฉ๊ณผ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 2 ์ œ2์žฅ ํ˜„๊ธˆํ๋ฆ„์— ๊ด€๋ จํ•œ ์ด๋ก ์  ๊ณ ์ฐฐ๊ณผ ๊ธฐ์กด์—ฐ๊ตฌ 3 ์ œ1์ ˆ ํ˜„๊ธˆํ๋ฆ„๊ณผ ํ˜„๊ธˆํ๋ฆ„๋ถ„์„ 3 1. ํ˜„๊ธˆํ๋ฆ„ 3 2. ํ˜„๊ธˆํ๋ฆ„๋ถ„์„ 4 3. ํ˜„๊ธˆํ๋ฆ„๋น„์œจ 5 ์ œ2์ ˆ ๊ธฐ์กด์—ฐ๊ตฌ์˜ ๊ฒ€ํ†  11 1. ๋ฏธ๋ž˜ํ˜„๊ธˆํ๋ฆ„์˜ˆ์ธก๋ ฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 11 2. ์ฃผ๊ฐ€๋ณ€๋™ ๋ฐ ์ฃผ๊ฐ€์ˆ˜์ต๋ฅ ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 13 3. ํ˜„๊ธˆํ๋ฆ„๋น„์œจ ๋ฐ ํ˜„๊ธˆํ๋ฆ„ํŠน์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 17 ์ œ3์žฅ ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ํ˜„๊ธˆํ๋ฆ„ ํ˜„ํ™ฉ๋ถ„์„ 19 ์ œ1์ ˆ ์™ธํ•ญํ•ด์šด์—…์˜ ํŠน์„ฑ 19 1. ํ•ด์šด์‚ฐ์—…์˜ ๊ฒฝ์˜์  ํŠน์„ฑ 19 2. ์™ธํ•ญํ•ด์šด์—…์˜ ํšŒ๊ณ„์  ํŠน์„ฑ 20 ์ œ2์ ˆ ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ํ˜„๊ธˆํ๋ฆ„์ฆ๊ฐ์˜ ํŠน์„ฑ 21 1. ์šฐ๋ฆฌ๋‚˜๋ผ ์ œ์กฐ์—…์˜ ํ˜„๊ธˆํ๋ฆ„์ฆ๊ฐ ํŠน์ง• 21 2. ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ํ˜„๊ธˆํ๋ฆ„์ฆ๊ฐ ํŠน์ง• 22 ์ œ3์ ˆ ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ์˜์—…ํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์„ฑ 25 1. ์šฐ๋ฆฌ๋‚˜๋ผ ์ œ์กฐ์—…์˜ ์˜์—…ํ˜„๊ธˆํ๋ฆ„ ํŠน์ง• 25 2. ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ์˜์—…ํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์ง• 26 ์ œ4์ ˆ ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ํˆฌ์žํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์„ฑ 29 1. ์šฐ๋ฆฌ๋‚˜๋ผ ์ œ์กฐ์—…์˜ ํˆฌ์žํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์ง• 29 2. ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ํˆฌ์žํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์ง• 30 ์ œ5์ ˆ ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ์žฌ๋ฌดํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์„ฑ 34 1. ์šฐ๋ฆฌ๋‚˜๋ผ ์ œ์กฐ์—…์˜ ์žฌ๋ฌดํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์ง• 34 2. ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ์žฌ๋ฌดํ˜„๊ธˆํ๋ฆ„์˜ ํŠน์ง• 34 ์ œ6์ ˆ ์šฐ๋ฆฌ๋‚˜๋ผ ์ƒ์žฅ ์™ธํ•ญ์„ ์‚ฌ์˜ ํ˜„๊ธˆํ๋ฆ„๋น„์œจ 38 1. ์ œ์กฐ์—… ํ˜„๊ธˆํ๋ฆ„๋น„์œจ 38 2. ํ˜„๊ธˆํ๋ฆ„์ˆ˜์ต์„ฑ๋น„์œจ 40 3. ํ˜„๊ธˆํ๋ฆ„์•ˆ์ •์„ฑ๋น„์œจ 45 4. ํ˜„๊ธˆํ๋ฆ„ํ‘œ ํ•ญ๋ชฉ๊ฐ„ ํ˜„๊ธˆํ๋ฆ„๋น„์œจ 51 ์ œ4์žฅ ํ˜„๊ธˆํ๋ฆ„์ •๋ณด์˜ ์œ ์šฉ์„ฑ์— ๊ด€ํ•œ ์‹ค์ฆ๋ถ„์„ 57 ์ œ1์ ˆ ์—ฐ๊ตฌ๋ชจํ˜•๊ณผ ์—ฐ๊ตฌ๊ฐ€์„ค 57 1. ์—ฐ๊ตฌ๋ชจํ˜• 57 2. ์—ฐ๊ตฌ๊ฐ€์„ค 59 ์ œ2์ ˆ ์‹ค์ฆ๋ถ„์„ 60 1. ์ž๋ฃŒ์ˆ˜์ง‘๊ณผ ๋ถ„์„๋ฐฉ๋ฒ• 60 2. ์ „ํ†ต์  ์žฌ๋ฌด๋น„์œจ๊ณผ ํ˜„๊ธˆํ๋ฆ„๋น„์œจ์˜ ์ •๋ณด ์œ ์‚ฌ์„ฑ ๊ฒ€์ฆ 62 3. ์ „ํ†ต์  ์žฌ๋ฌด๋น„์œจ๊ณผ ํ˜„๊ธˆํ๋ฆ„๋น„์œจ์˜ ์ •๋ณด ์œ ์šฉ์„ฑ ๊ฒ€์ฆ 63 ์ œ5์žฅ ๊ฒฐ๋ก  70 ์ œ1์ ˆ ์‹ค์ฆ๋ถ„์„๊ฒฐ๊ณผ ์š”์•ฝ 70 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์™€ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ 71 ์ฐธ๊ณ ๋ฌธํ—Œ 7

    ์„ธ๊ณ„GDP์—์„œ ๊ฐ ๊ตญ ๋น„์ค‘์˜ ๊ฒฐ์ •์š”์ธ : ํ™˜์œจ๊ณผ ์ด์œค์œ ์ถœ์„ ์ค‘์‹ฌ์œผ๋กœ 1์ธ๋‹น์†Œ๋“ ๊ฒฐ์ •์š”์ธ๊ณผ ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์ œํ•™๋ถ€, 2014. 8. ์ด๊ทผ.๋ณธ ๋…ผ๋ฌธ์€ ์„ธ๊ณ„GDP์—์„œ ๊ฐ ๊ตญ์˜ ๋น„์ค‘(GDP ๋น„์ค‘)๊ณผ 1์ธ๋‹น ์†Œ๋“์˜ ๊ฒฐ์ •์š”์ธ์„ ๋น„๊ตํ•˜๋Š” ์ƒˆ๋กœ์šด ์‹œ๊ฐ์„ ํ†ตํ•˜์—ฌ ๊ตญ๊ฐ€์˜ ์ถ”๊ฒฉ, ์ถ”์›”, ์ถ”๋ฝ ๋“ฑ ๋‹ค์–‘ํ•œ ๊ฒฝ์ œ์„ฑ์žฅํ˜„์ƒ์˜ ๊ฒฐ์ •์š”์ธ์„ ์žฌ์กฐ๋ช…ํ•˜์˜€๋‹ค. 1์ธ๋‹น ์†Œ๋“์€ ๊ฐœ๊ฐœ์ธ์˜ ์‚ถ์˜ ์งˆ์„ ๋ฐ˜์˜ํ•˜๋Š” ๋ฐ˜๋ฉด, GDP๋น„์ค‘์€ ๊ตญ๊ฐ€์˜ ๊ฒฝ์ œ๊ทœ๋ชจ ๋ฐ ๊ฒฝ์ œ๋ ฅ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค๋Š” ์ ์—์„œ ์ƒํ˜ธ๋ณด์™„์ ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ์ฃผ ๊ฐ€์„ค์€ ๊ฒฝ์ œ์„ฑ์žฅ์„ ๋‚˜ํƒ€๋‚ด๋Š” 1์ธ๋‹น ์†Œ๋“๊ณผ GDP๋น„์ค‘์˜ ๊ฒฐ์ •์š”์ธ์ด ๋‹ค๋ฅด๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ 1์ธ๋‹น ์†Œ๋“์„ ๊ฒฐ์ •์ง“๋Š” ์ „ํ†ต์ ์ธ ์„ฑ์žฅ๊ฒฐ์ •์š”์ธ๋“ค์ด GDP๋น„์ค‘์—๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์ง€ ์•Š์€ ๋ฐ˜๋ฉด, ๊ทธ๊ฒƒ๋“ค์„ ์„ธ๊ณ„์ธ๊ตฌ๋Œ€๋น„ ๋น„์ค‘, ์„ธ๊ณ„์ž๋ณธ๋Œ€๋น„ ๋น„์ค‘๊ณผ ๊ฐ™์€ ๋น„์ค‘๋ณ€์ˆ˜๋“ค๋กœ ํ‘œํ˜„ํ•˜์˜€์„ ๋•Œ ์œ ์˜ํ•จ์„ ๋ณด์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ์ž๊ตญ ํ†ตํ™”์˜ ํ‰๊ฐ€์ ˆํ•˜, ์ œ๋„์˜ ์งˆ์  ์ˆ˜์ค€, ๊ฐœ๋ฐฉ๋„, ์™ธ๊ตญ์ž๋ณธ ๋“ฑ์ด 1์ธ๋‹น ์†Œ๋“์—๋Š” ๊ธ์ •์ ์ผ ์ˆ˜ ์žˆ์ง€๋งŒ, GDP๋น„์ค‘์—๋Š” ๊ทธ๋ ‡์ง€ ์•Š์„ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋ฐ˜๋ฉด, GDP๋น„์ค‘ ๋ณ€ํ™”์—๋Š” ์ž๊ตญํ†ตํ™”์˜ ํ‰๊ฐ€์ ˆํ•˜, ์ „์„ธ๊ณ„ ์ˆ˜์ถœ์—์„œ์˜ ๋น„์ค‘, ์ „์„ธ๊ณ„ ์ž๋ณธํ๋ฆ„์—์„œ ์™ธ๊ตญ์ž๋ณธ์œ ์น˜๋น„์ค‘๊ณผ ๊ฐ™์ด ๊ธ€๋กœ๋ฒŒ ๊ฒฝํ•ฉ์„ฑ๊ณผ ๊ฒฝ์ œ์„ฑ์žฅ์˜ ์ƒ๋Œ€์  ์„ฑ๊ณผ๋ฅผ ๋ฐ˜์˜ํ•œ ๋ณ€์ˆ˜๋“ค์ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•จ์„ ๋ณด์˜€๋‹ค. ํ•œํŽธ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ํ•ด์™ธ์ž๋ณธ์˜ ํ๋ฆ„ ์ค‘ ์ด์œค์œ ์ถœํ๋ฆ„์— ์ฃผ๋ชฉํ•˜์—ฌ ๊ฒฝ์ œ์„ฑ์žฅ์—์˜ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ˆ˜๋ฆฝ, ์ฆ๋ช…ํ•œ ๊ฐ€์„ค์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๊ฐœ๋„๊ตญ์—์„œ๋Š” ์‹ ๊ทœ๋กœ ์œ ์น˜ํ•œ ์™ธ๊ตญ์ž๋ณธ๋ณด๋‹ค ๋” ๋งŽ์€ ์–‘์˜ ์ด์œค์ด ๊พธ์ค€ํžˆ ๊ตญ์™ธ๋กœ ์œ ์ถœ๋˜์–ด ๊ฒฝ์ œ์„ฑ์žฅ์˜ ์†๋„๊ฐ€ ์ €ํ•˜๋œ๋‹ค๋Š” ๊ฒƒ๊ณผ, ์ด๋Ÿฌํ•œ ์„ฑ์žฅํšจ๊ณผ๋Š” ํˆฌ์ž์œ ์น˜๊ตญ์˜ ๋‚ด์žฌ์  ์—ญ๋Ÿ‰, ์ฆ‰ ํก์ˆ˜์—ญ๋Ÿ‰์ด๋‚˜ ์ธ์ ์ž๋ณธ ์ˆ˜์ค€ ๋ฐ ๊ธฐ์ˆ ์—ญ๋Ÿ‰ ์ˆ˜์ค€์— ๋”ฐ๋ผ ์ƒ์ดํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด์— ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์™ธ๊ตญ์ž๋ณธ์˜ ์‹ ๊ทœ์œ ์ž…์ด ๋งŽ์„์ˆ˜๋ก ๊ฒฝ์ œ์„ฑ์žฅ์— ๊ธ์ •์ ์ด๊ณ  ์ด์œค์œ ์ถœํ๋ฆ„์ด ์ปค์งˆ์ˆ˜๋ก ๊ฒฝ์ œ์„ฑ์žฅ์— ๋ถ€์ •์ ์ด์ง€๋งŒ, ๊ทธ ํšจ๊ณผ์˜ ํฌ๊ธฐ๋Š” ํˆฌ์ž์œ ์น˜๊ตญ์˜ ๋ฐœ์ „์ˆ˜์ค€, ์ฆ‰ 1์ธ๋‹น ์†Œ๋“, ๊ณ ๋“ฑ์ธ์ ์ž๋ณธ, ์ธ๊ตฌ๋‹น ํŠนํ—ˆ์ˆ˜๋กœ ์ธก์ •ํ•œ ์ˆ˜์ค€์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ฐœ๋„๊ตญ์—์„œ ์ด์œค์œ ์ถœํ๋ฆ„์˜ ๋ถ€์ •์  ํšจ๊ณผ๊ฐ€ ์ƒ์‡„๋˜๋Š” ์ตœ์†Œ ๊ธฐ์ค€์ ์ด ์™ธ๊ตญ์ธ์ง์ ‘ํˆฌ์ž๊ฐ€ ๊ฒฝ์ œ์„ฑ์žฅ์— ์–‘์˜ ํšจ๊ณผ๋ฅผ ๊ฐ–๊ธฐ ์‹œ์ž‘ํ•˜๋Š” ์ตœ์†Œ ๊ธฐ์ค€์ ๋ณด๋‹ค ํ›จ์”ฌ ๋†’์€ ์ˆ˜์ค€์ž„์„ ๋ฐํ˜”๋‹ค. ์ด๋Š” ์™ธ๊ตญ์ž๋ณธ์œ ์ž…๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ด์œค์œ ์ถœ๊ณผ ๊ฐ™์€ ์ž๋ณธ์œ ์ถœ์ด ๊ฐœ๋„๊ตญ์˜ ๊ฒฝ์ œ์„ฑ์žฅ์„ ์„ค๋ช…ํ•˜๋Š” ์ค‘์š”ํ•œ ๊ฒฐ์ •์š”์ธ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค.This dissertation revisits the question of what determines economic growth in the phenomenon of catching up, forging ahead, and lagging behind of nations, with a new focus on the comparison of the determinants of each nations share in world GDP and per capita income. These two indicators are complementary. The latter represents a peoples standard of living and the former represents economic size (power) of a nation. The grand hypothesis is that determinants of these two different aspects of economic growth are markedly different. This dissertation shows that conventional growth variables are not statistically significant in GDP share equation, but they must be transformed into shares, such as the share of a country in world population or human capital. More importantly, this dissertation shows that although the undervaluation of currency of each nation may promote the growth in per capita income, it tends to reduce each nations share in world GDP because undervaluation depreciates GDP of a country measured at market exchange rate. This dissertation also finds the variables of institutional quality, openness, and foreign capital inflows are often significant in growth of per capita income, but not in GDP share equations. Specifically, the dissertation runs a regression of each nations share in world GDP as a dependent variable with explanatory variables, such as exchange rate undervaluation, export share in world export, foreign capital inflow share in global capital inflow, as well as other conventional growth variables in world share forms, and finds those determinants reflecting rivalry and relative performance are statistically associated with GDP share change. Having verified the new determinants of each nations share in world GDP, this dissertation investigates the effect of various foreign capital flows, including repatriated profits. A motivation for this question is a hypothesis that developing countries tend to face slower economic growth because they consistently encounter more outbound capital flows in general, in the form of interests payments and dividends, than new inbound capital flows, and the effect of such flows might depend upon the indigenous capability of each nation, such as absorption capacity, level of human or technological capabilities. Then, empirical analyses verify the above hypothesis. The dissertation finds that although hosting more foreign capital is good for economic growth, repatriated profit tends to be negatively related to economic growth in the South, and foreign capital inflow and repatriated profit have different effects on economic growth based on the development level of countries, with certain threshold values identified in terms of level of per capita income, advanced human capital, and number of patents. Moreover, this dissertation finds that this threshold is much higher than that of FDI in which the host developing countries obtain the positive effect from FDI. This result implies reverse financial flow out of developing countries in the form of repatriated profit and not that financial flow itself may be one of the important causes of the growth problems in the South.Chapter1. Introduction 1 I. Motivation 1 II. Why consider the economic size and its share in world GDP: How are they different from GDP per capita? 3 III. Hypotheses of dissertation 18 IV. Data and Methodology 20 V. Chapter conclusion 22 Chapter2. What determines each nations share in world GDP 24 I. Introduction: Production function and the basic growth equation in econometrics 24 II. Three steps to generate the equation for GDP share change 26 1. Nominal GDP growth equation 26 2. Relative growth performance equations 30 3. The each nations share in world GDP 35 III. Literature and Hypotheses: Determinants of each nations share in world GDP 38 1. Typical determinants in share form 38 2. Other determinants that reflect relative performance and rivalry: Export share, foreign capital share, and undervaluation 41 IV. GDP share change equation: Estimation results 45 1. Baseline and Different Determinants of GDP share and GDP per capita growth rate 45 2. Robust check โ€“ Share variables in Barro equation 53 3. Robust check โ€“ System GMM 55 V. Chapter conclusion 58 Chapter3. Effects of foreign capital flows and repatriated profit in developed and developing countries 60 I. Introduction 60 II. Literature review and Hypothesis of chapter: Foreign capital and economic growth 62 III. Methodology and Data 70 1. Estimation methodology 70 2. Data sources and data description 71 IV. Estimation results 74 1. Estimation results of all countries 74 2. Estimation results of developed countries 77 3. Estimation results of developing countries 78 4. Threshold study 86 5. System-GMM estimation results โ€“ Robustness check 90 V. Policy Implication and Chapter conclusion 92 Chapter4. Summary and Concluding Remarks 95 References 99 Appendix 111Docto

    ํ•œ๊ตญ๋†์—…๊ณผ ๋†์—…ํ˜‘๋™์กฐํ•ฉ

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    ํ–‰์‚ฌ๋ช… : ์ค‘๊ตญ NDRC ๋†์ดŒ๊ฐœ๋ฐœ ์—ฐ์ˆ˜๊ณผ

    ํ‘œ๋ฉด ์ฒ˜๋ฆฌ๋œ ํด๋ฆฌ๋น„๋‹์•Œ์ฝ”์˜ฌ/์‹ค๋ฆฌ์นด ์ „๊ธฐ๋ฐฉ์‚ฌ ๋งคํŠธ์˜ ์ œ์กฐ์™€ ํ•ญ๊ท  ํŠน์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€(์—๋„ˆ์ง€ํ™˜๊ฒฝ ํ™”ํ•™์œตํ•ฉ๊ธฐ์ˆ ์ „๊ณต), 2013. 8. ์žฅ์ •์‹.์กธ-๊ฒ” ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ ํด๋ฆฌ๋น„๋‹์•Œ์ฝ”์˜ฌ๊ณผ ์‹ค๋ฆฌ์นด ๊ฐ„์˜ ๊ฐ€๊ต๋ฅผ ์ด๋ฃฌ ์šฉ์•ก์„ ์ „๊ธฐ๋ฐฉ์‚ฌ๋ฅผ ํ†ตํ•ด ๋งคํŠธ ํ˜•ํƒœ๋กœ ์ œ์กฐํ•˜๊ณ  ์‹ค๋ž€์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ผ ํ‘œ๋ฉด์„ ๊ฐœ์งˆ ํ•˜์—ฌ ํ•ญ๊ท ์„ฑ ํ‘œ๋ฉด์— ์‘์šฉํ•˜์˜€๋‹ค. ์นœ์ˆ˜์„ฑ ๋งคํŠธ์˜ ํ‘œ๋ฉด์€ ์‹ค๋ž€์˜ ์•Œํ‚ฌ๊ธฐ์˜ ๋„์ž…์— ๋”ฐ๋ผ ์†Œ์ˆ˜์„ฑ์œผ๋กœ ๊ฐœ์งˆ๋˜์—ˆ์œผ๋ฉฐ, ๋”๋ถˆ์–ด 4์ฐจ ์•„๋ฏผ์„ ์ง€๋‹Œ ์‹ค๋ž€์ด ์ฒ˜๋ฆฌ๋œ ํ‘œ๋ฉด์—์„œ๋Š” ์งˆ์†Œ์˜ ์–‘์„ฑ ์ „ํ•˜๋ฅผ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ๋žŒ ์Œ์„ฑ์ธ ๋Œ€์žฅ๊ท , ๊ทธ๋žŒ ์–‘์„ฑ์ธ ํ™ฉ์ƒ‰ํฌ๋„์ƒ๊ตฌ๊ท ๊ณผ ๋งคํŠธ์™€์˜ ํ‘œ๋ฉด ์ ‘์ด‰, ์‹œ๊ฐ„ ๊ทธ๋ฆฌ๊ณ  ๋ฐ˜๋ณต ์‹คํ—˜์„ ์ง„ํ–‰ ํ•จ์œผ๋กœ์จ ํ•ญ๊ท ์„ฑ๊ณผ ๋‚ด๊ตฌ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์™€ ๊ฐ™์ด ์ œ์กฐ๋œ 4์ฐจ ์•„๋ฏผ-ํด๋ฆฌ๋น„๋‹์•Œ์ฝ”์˜ฌ/์‹ค๋ฆฌ์นด ์ „๊ธฐ๋ฐฉ์‚ฌ ๋งคํŠธ๋Š” 150๋ถ„ ๋™์•ˆ ๊ท ๊ณผ์˜ ์ ‘์ด‰ ์—์„œ 99.9%์ด์ƒ์˜ ์šฐ์ˆ˜ํ•œ ํ•ญ๊ท ์„ฑ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ๋ฐ˜๋ณต์‹คํ—˜์—์„œ๋„ 99%์ด์ƒ์˜ ์„ฑ๋Šฅ์„ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹คThe fabrication of surface modified antibacterial electrospun mat is a promising material for enhanced antibacterial performance. In this study, PVA-Silica (P-S) electrospun mat was fabricated via sol-gel electrospinning and quaternary ammonium silane (QAS) and octadecyltrimethoxy silane (ODS) were applied for the modification of electrospun mat. The chemical bonding and surface elements that fabricated material was represented through analysis of the EDS and IR, and properties of the modified surface was able to be confirmed by contact angle measurements via water droplets. The surface of ODS modified electrospun mat showed hydrophobic characteristics and QAS modified P-S represented not only hydrophobic property but also positive charge. The antibacterial performance was evaluated using the kinetic test method. Interestingly, the contact time after 150min, gram-negative (E. coli) and gram-positive bacteria (S. aureus) were shown to be killed by charge-charge interaction with the surface of QAS-P-S electrospun mat. Moreover, the mat maintained the antibacterial performance of more than 99% during recycling. It is expected to be applicable in various fields using antibacterial activity of high-durability, such as membrane, filter, and medical device.Contents Chapter 1. Introduction 1 1.1 Antibactrial field 1 1.2 Antibacterial nanomaterials 2 1.3 Synthesis of antibacterial nanomaterials 3 1.4 Synthetic method of antibacterial nanomaterials using electrospinning and surface modification 5 1.5 Objective of this study 8 Chapter 2. Experimental 9 2.1 Materials 9 2.2 Preparation of PVA-Silica gel solution 9 2.3 Preparation of electrospun mat 10 2.4 Surface Modification of PVA-Silica electrospun mat 11 2.5 Antibactrial test of modified PVA-Silica electrospun mats 11 2.6 Characterization 12 Chapter 3. Results and discussion 14 3.1 Fabrication of QAS-PVA-Silica electrospun mat 14 3.1.1 Synthetic process of QAS-PVA-Silica electrospun mat 14 3.1.2 SEM images of PVA-Silica electrospun mats with different concentration ratio 17 3.1.3 SEM images of PVA, PVA-Silica and silane modified PVA-Silica electrospun mats 20 3.2 Characterization of QAS-PVA-Silica electrospun mat 23 3.2.1 Surface energy analysis of silane modified PVA-Silica electrospun mats 23 3.2.2 FT-IR and EDS analysis of silane modified electrospun mats 27 3.2.3 Thermal analysis of silane modified electrospun mats 32 3.3 Application of QAS-PVA-Silica electrospun mats for antibacterial performance 34 Chapter 4. Conclusion 42 References 44 Abstract 52Maste

    ํšจ์œจ์ ์ธ ์™„์ „ํžˆ ์—ฐ๊ฒฐ๋œ ์‹ ๊ฒฝ๋ง ์ถ”๋ก ๊ฐ€์†๊ธฐ๋ฅผ ์œ„ํ•œ ์ŠคํŒŒ์Šค ๋งคํŠธ๋ฆญ์Šค ํ˜•์‹ ๋ฐ ์•„ํ‚คํ…์ฒ˜

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    DoctorLSTM ๋ฐ Transformer๋Š” ์Œ์„ฑ์ธ์‹, ๊ธฐ๊ณ„๋ฒˆ์—ญ ๋ฐ ์–ธ์–ด ๋ชจ๋„ฌ๋ง๊ณผ ๊ฐ™์€ ์‹œ๊ณ„์—ด ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ๋„ฌ๋งํ•˜๊ณ  ๋ถ„์„ํ•˜๋Š”๋ฐ ๋„๋ฆฌ ํ™œ์šฉ๋œ๋‹ค. LSTM๊ณผ Transformer๋Š” fully- connected ๊ธฐ๋ฐ˜์˜ ๋‰ด๋Ÿด๋„คํŠธ์›Œํฌ์ด๋ฉฐ ์—ฐ์‚ฐ์˜ ๋Œ€๋ถ€๋ถ„์€ ๋งคํŠธ๋ฆญ์Šค์™€ ๋ฒกํ„ฐ์˜ ๊ณฑ(MxV) ์ด๋‹ค. ์›จ์ดํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ์žฌ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” CNN๊ณผ ๋‹ฌ๋ฆฌ LSTM๊ณผ Transformer์—์„œ ๋Š” FC์—ฐ์‚ฐ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์›จ์ดํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ์žฌ์‚ฌ์šฉํ•  ์ˆ˜ ์—†๋‹ค. ๊ทธ๋ž˜์„œ ๋ฉ”๋ชจ๋ฆฌ bandwidth์— ๋”ฐ๋ผ์„œ ์™ธ๋ถ€ ๋ฉ”๋ชจ๋ฆฌ๋กœ ๋ถ€ํ„ฐ ๊ฐ€์†๊ธฐ ๋‚ด๋ถ€์˜ ๋ฒผํผ๋กœ ์›จ์ดํŠธ ๋ฐ ์ดํ„ฐ๋ฅผ ๋กœ๋“œํ•˜๋Š” ๊ฒƒ์ด ์„ฑ๋Šฅ์˜ bottleneck์ด๋œ๋‹ค. ์ด ๋ฌธ์ œ๋Š” ๋‰ด๋Ÿด๋„คํŠธ์›Œํฌ ์••์ถ•์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” pruning์„ ํ†ตํ•ด์„œ ๋ฐ์ดํ„ฐ ๋กœ๋“œ์— ๋Œ€ํ•œ ๋ถ€๋‹ด๊ณผ ์ €์žฅ์šฉ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ pruning์„ ์ˆ˜ํ–‰ํ•˜๋ฉด ์›จ์ดํŠธ ๋งคํŠธ๋ฆญ์Šค๋Š” sparseํ•ด์ง€๊ณ  ๊ธฐ์กด์˜ MxV์—ฐ ์‚ฐ์€ sparse MxV (spMxV)์—ฐ์‚ฐ์œผ๋กœ ๋ฐ”๋€Œ๊ฒŒ๋œ๋‹ค. sparse ๋งคํŠธ๋ฆญ์Šค๋Š” โ€™0โ€™์„ ๋กœ๋“œํ•˜๋Š” ์‚ฌ์ดํด์„ ์ค„์ด๊ธฐ ์œ„ํ•ด sparse ๋งคํŠธ๋ฆญ์Šค ํฌ๋ฑƒ (๊ฐ€๋ น CSC,CSR ํฌ๋ฑƒ)์œผ๋กœ ์ €์žฅ๋˜๊ฒŒ ๋œ๋‹ค. ๊ธฐ์กด์˜ sparse ๋งคํŠธ๋ฆญ์Šค ํฌ๋ฑƒ์œผ๋กœ ์ €์žฅ๋œ ์›จ์ดํŠธ ๋ฐ์ดํ„ฐ์™€ ์••๋ ฅ ๋ฐ์ดํ„ฐ์˜ ๊ณฑ ์…ˆ (spMxV)์ด ์ˆ˜ํ–‰๋ ๋•Œ ํฌ๊ฒŒ ๋‘๊ฐ€์ง€ ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ์ฒซ๋ฒˆ์งธ ๋ฌธ์ œ๋Š” ๊ฐ๊ฐ์˜ PE ์— ํ• ๋‹น๋˜๋Š” ๊ณ„์‚ฐ ๋กœ๋“œ๊ฐ€ ๋‹ค๋ฅด๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. PE์— ๊ณ„์‚ฐ ๋กœ๋“œ๊ฐ€ ๋ถˆ๊ท ์ผํ•˜๊ฒŒ ํ• ๋‹น๋˜๋ฉด PE๋งˆ๋‹ค ์—ฐ์‚ฐ์„ ์™„๋ฃŒํ•˜๋Š” ์‹œ๊ฐ„์˜ ์ฐจ์ด๊ฐ€ ์ปค์ง€๊ฒŒ ๋œ๋‹ค. ๊ฐ€์žฅ ๋Šฆ๊ฒŒ ์—ฐ์‚ฐ์„ ๋๋‚ด๋Š” PE ๊ฐ€ ์ „์ฒด ์‹œ์Šคํ…œ latency์˜ bottleneck์ด ๋˜์–ด ์„ฑ๋Šฅ์„ ์ œํ•œ๋œ๋‹ค. PE์—์„œ spMxV์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์›จ์ดํŠธ ๋ฐ์ดํ„ฐ์™€ ์ด ๋ฐ์ดํ„ฐ์— ํ•ด๋‹นํ•˜๋Š” ์••๋ ฅ ๋ฐ์ดํ„ฐ๊ฐ€ ํ•„์š”ํ•˜ ๋‹ค. ์ด๋•Œ ์›จ์ดํŠธ ๋ฐ์ดํ„ฐ๊ฐ€ sparseํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ณฑ์…ˆ์— ํ•„์š”ํ•œ ์••๋ ฅ ๋ฐ์ดํ„ฐ ์š”์†Œ๋„ ๋ถˆ๊ทœ์น™ํ•œ ์ˆœ์„œ๋กœ ๋ฉ”๋ชจ๋ฆฌ์— ์š”์ฒญ๋œ๋‹ค. ๋งŒ์•ฝ ์š”์ฒญ๋˜๋Š” ์••๋ ฅ ๋ฐ์ดํ„ฐ ์š”์†Œ๊ฐ€ PE๋‚ด๋ถ€์˜ ๋กœ์ปฌ ๋ฒผํผ์— ๋ฏธ๋ฆฌ ๋กœ๋“œ ๋˜์–ด์žˆ์ง€ ์•Š๋‹ค๋ฉด PE๋Š” ์ ์ ˆํ•œ ๋ฐ์ดํ„ฐ๊ฐ€ ์ค€๋น„๋ ๋•Œ ๊นŒ์ง€ ์Šค ํ†จ๋œ๋‹ค. ์ด ๋‘๊ฐ€์ง€ ๋ฌธ์ œ๋“ค์€ PE์˜ ํ™œ์šฉ๋„๋ฅผ ๋‚ฎ์ถ”๊ฒŒ ๋˜๊ณ  ๊ฒฐ๊ตญ ์ด๋กœ ์ธํ•ด latency์™€ ํŒŒ์›Œ์†Œ๋ชจ๋Š” ์ฆ๊ฐ€ํ•˜๊ฒŒ๋œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ์„ธ๊ฐ€์ง€ ์ข…๋ฅ˜์˜ sparse ๋งคํŠธ๋ฆญ์Šค ํฌ๋ฑƒ ๊ณผ ๊ทธ ํฌ๋ฑƒ์— ๋งž๋Š” ์•„ํ‚คํ…์ฒ˜๋ฅผ ํ•จ๊ป˜ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ๋ฒˆ์งธ๋กœ ์šฐ๋ฆฌ๋Š” LSTM ์ถ”๋ก ๊ณผ์ •์„ ๊ฐ€์†ํ™” ํ•˜๊ธฐ์œ„ํ•œ Compressed and Balanced Sparse Row (CBSR) ํฌ๋ฑƒ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ํฌ๋ฑƒ์€ PE๊ฐ„์˜ ๊ณ„์‚ฐ ๋กœ๋“œ์˜ ๋ถˆ๊ท ํ˜•์„ ์ตœ์†Œํ™” ํ•˜๋Š”๋ฐ ์ดˆ์ ์„ ๋งž์ถ˜๋‹ค. ๋˜ํ•œ ํฌ๋ฑƒ ์ƒ์„ฑ ๊ณผ์ • ์ค‘ ์›จ์ดํŠธ ๋งคํŠธ๋ฆญ์Šค์˜ ๊ฐ€๋กœํ–‰ ์ˆœ์„œ๋ฅผ ๋ฐ”๊พธ๊ฒŒ ๋˜๋ฉด์„œ ๋ฐœ์ƒํ•˜๋Š” ์˜ค๋ฒผํ—ค๋“œ ๋ฅผ ๊ฐ„๋‹จํ•œ ๋„คํŠธ์›Œํฌ ๋ณ€ํ™˜์„ ์ œ์•ˆํ•˜์—ฌ ์—†์•ค๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ CBSR ํฌ๋ฑƒ์€ ๊ธฐ์กด์˜ CSR/CSC ํฌ๋ฑƒ์— ๋น„ํ•ด ๊ฐ€์†๊ธฐ์˜ ์ฒ˜๋ฆฌ๋Ÿ‰์„ 16โˆผ38% ํ–ฅ์ƒ์‹œํ‚ค๊ณ , ์—๋„ˆ์ง€๋ฅผ 9โˆผ22% ์ค„์ธ๋‹ค. ๋‘๋ฒˆ์งธ๋กœ ์šฐ๋ฆฌ๋Š” CBSR ํฌ๋ฑƒ์„ ๋”์šฑ ๋ฐœ์ „์‹œํ‚จ Rearranged Compressed Sparse Column (RCSC) ํฌ๋ฑƒ์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด ํฌ๋ฑƒ์€ ๋กœ๋“œ ๋ถˆ๊ท ํ˜• ๋ฌธ์ œ๋งŒ ์™„ํ™”ํ•œ ๋ฐ˜๋ฉด์— ์ด ํฌ๋ฑƒ์€ ์••๋ ฅ ๋ฐ์ดํ„ฐ ๋กœ๋“œ ๋ฏธ์Šค ๋ฌธ์ œ์™€ ๋กœ๋“œ ๋ถˆ๊ท ํ˜• ๋ฌธ์ œ๋ฅผ ํ†ต์‹œ์— ํ•ด๊ฒฐํ•œ ๋‹ค. sparse ๋งคํŠธ๋ฆญ์Šค ํฌ๋ฑƒ์ด ์••๋ ฅ ์š”์†Œ๊ฐ€ ์š”์ฒญ๋˜๋Š” ๋ฉ”๋ชจ๋ฆฌ ์ฃผ์†Œ์˜ spatial locallity ๋ฅผ ๋†’์ด์ง€๋งŒ ์™„์ „ํžˆ ์••๋ ฅ ์š”์†Œ์˜ ์Šคํ†จ์„ ๋ง‰์„ ์ˆ˜๋Š” ์—†๋‹ค. ๊ทธ๋ž˜์„œ ํฌ๋ฑƒ์œผ๋กœ ์ปค๋ฒผํ•˜์ง€ ๋ชปํ•˜๋Š” ์Šคํ†จ์€ ์ƒˆ๋กœ์šด ๊ณ„์ธต์  ๋ฒผํผ๊ตฌ์กฐ์˜ ์•„ํ‚คํ…์ฒ˜๋ฅผ ์ œ์•ˆํ•จ์œผ๋กœ์จ ์ตœ์†Œํ™” ํ•œ๋‹ค. ์ด ์•„ํ‚คํ…์ฒ˜๋Š” ๋กœ์ปฌ ๋ฒผํผ์˜ hit rate์„ ๋†’์ด๊ธฐ ์œ„ํ•ด์„œ ์ ์€ ์‚ฌ์ดํด ๋™์•ˆ ์ตœ๋Œ€ํ•œ ๋„“์€ ๋ฒ”์œ„์˜ ๋ฉ”๋ชจ๋ฆฌ ์ฃผ์†Œ๋ฅผ ์ ‘๊ทผ ํ•  ์ˆ˜ ์žˆ๋Š” ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ œ์•ˆ๋œ ํฌ๋ฑƒ๊ณผ ์•„ํ‚คํ…์ฒ˜๋Š” ๋กœ์ปฌ ๋ฒผํผ์˜ hit rate๊ณผ ์†๋„๋ฅผ ๊ฐ๊ฐ 30โˆผ230%, 27โˆผ84% ํ–ฅ์ƒ ์‹œํ‚จ๋‹ค. ์•ž์„œ ์ œ์‹œํ•œ ๋‘ sparse ๋งคํŠธ๋ฆญ์Šค ํฌ๋ฑƒ์€ ์˜ค์ง LSTM์— ์ ์šฉ๋˜๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์„ธ๋ฒˆ ์งธ๋กœ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์€ ๊ธฐ๊ณ„๋ฒˆ์—ญ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” Transformer ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ sparse ๋งคํŠธ๋ฆญ์Šค ํฌ๋ฑƒ๊ณผ ์•„ํ‚คํ…์ฒ˜์ด๋‹ค. Set-Associative RCSC (SA-RCSC) ํฌ๋ฑƒ์€ RCSC ํฌ๋ฑƒ์„ ๋ณ€ํ˜•ํ•˜์—ฌ Transformer์—๋„ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก ํ–ˆ์„ ๋ฟ๋งŒ ์•„ ๋‹ˆ๋ผ ๋งŽ์€ PE๊ฐ€ ๊ฐ€์†๊ธฐ์— ์„ค๊ณ„๋˜์–ด ์žˆ์–ด๋„ PE์˜ ํ™œ์šฉ๋ฅ ์ด ๊ฐ์†Œํ•˜์ง€ ์•Š๋„๋ก ํ•œ๋‹ค. ํŠนํžˆ latency ๋Œ€๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•˜๋Š” ๋””์ฝ”๋”ฉ ์‹œ๊ฐ„์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ž์„ธํ•œ Transformer ๋ถ„์„์„ ํ†ตํ•ด ๋ถˆํ•„์š”ํ•œ ์—ฐ์‚ฐ์„ ๊ฑด๋„ˆ๋›ฐ๊ฒŒ ๋œ๋‹ค. ๋˜ํ•œ Transformer์—์„œ ํ•„์š”ํ•œ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ๋งคํŠธ๋ฆญ์Šค ๊ณฑ์…ˆ์„ ์ˆ˜ํ–‰ ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋“œ์›จ์–ด flexibilty๋ฅผ ๊ฐ€์ง€๋„๋ก ์•„ํ‚คํ… ์ฒ˜(OPTIMUS๋ผ ๋ถˆ๋ฆฌ๋Š”)๊ฐ€ ๋””์ž์ธ๋˜์—ˆ๋‹ค. CPU, GPU, ์ „์šฉ ํ•˜๋“œ์›จ์–ด๋กœ ๋””์ง€์ธ๋œ ๋Œ€์กฐ๊ตฐ๊ณผ ๋น„๊ตํ•ด์„œ OPTIMUS์˜ latency๋Š” 41.62ร—, 24.23ร—, 16.01ร— ๋งŒํผ ๋” ์งง ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ๋น„๊ต๋Œ€์ƒ๋“ค์— ๋น„ํ•ด ์ฒ˜๋ฆฌ๋Ÿ‰๋„ ๊ฐ๊ฐ 43.35ร—, 25.45ร—, 19.00ร— ๋งŒํผ ๋” ๋†’๋‹ค.The LSTM and the Transformer are widely used neural network models for modeling or analyzing time-varying data such as speech recognition, machine translation, and language modeling. The main operation of the LSTM and the Transformer is changed from dense matrix-vector multiplication to sparse matrix- vector multiplication after weight matrix pruning which is widely exploited in deep learning. Although the pruning greatly reduces memory requirements, the sparse weight matrix causes some issues in the matrix multiplication. The first issue is that the computational load to be processed is distributed differently for each PE. This disproportion between PEs significantly reduces the average utilization of PE. The second issue is that many stalls can occur because the input vector elements required for multiplication in the PE are not prepared in the local buffer. As with the first issue, these stalls lead to lower utilization of the PE, resulting in increased latency. In this dissertation, three types of sparse matrix formats and architectures are proposed to mitigate these issues. First, we propose the Compressed and Bal- anced Sparse Row (CBSR) format to improve the inference speed of the LSTM accelerator. This format focuses on minimizing load imbalance over PEs. Also, the network transformation is presented to eliminate the additional overhead in- curred by CBSR format generation. As a result, the LSTM accelerator has a 16โˆผ38% better throughput and 9โˆผ22% less energy than conventional CSR/CSC formats. Second, we present the advanced Rearranged Compressed Sparse Col- umn (RCSC) format of the CBSR format. This format also aims to accelerate the LSTM, while the CBSR format focuses on only one issue (load imbalance), while this format can mitigate both issues (load imbalance and input load miss). In addition, stalls that are not covered by the RCSC format are minimized by suggesting a new architecture. The architecture has a hierarchical buffer to search a wider range of memory addresses in a minimum number of cycles, increasing the hit rate at the local buffer. The proposed format improves spatial locality when accessing input vectors, increasing the hit rate in local buffers by 30โˆผ230% and achieving a 27โˆผ84% speed-up. The previous two sparse matrix formats could only be applied to LSTM. Third, we propose a new format, named Set-Associative RCSC (SA-RCSC), and architecture, named OPTIMUS, that can mitigate sparse issues even in the Transformer network inference. The SA-RCSC format enables high PE utilization even if a large MAC is implemented in the accelerator. We also present skipping redundant computations to improve the performance of the Transformer decoding process. OPTIMUS, a hardware accelerator, is de- signed with the flexibility to support various types of matrix multiplication in the Transformer neural networks. The latency of OPTIMUS is 41.62ร—, 24.23ร—, 16.01ร— smaller than that of CPU, GPU and the baseline custom hardware, re- spectively. In addition, the throughput is 43.35ร—, 25.45ร—, 19.00ร— higher than the comparisons

    Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators

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    The long short-term memory (LSTM) is a widely used neural network model for dealing with time-varying data. To reduce the memory requirement, pruning is often applied to the weight matrix of the LSTM, which makes the matrix sparse. In this paper, we present a new sparse matrix format, named rearranged compressed sparse column (RCSC), to maximize the inference speed of the LSTM hardware accelerator. The RCSC format speeds up the inference by: 1) evenly distributing the computation loads to processing elements (PEs) and 2) reducing the input vector load miss within the local buffer. We also propose a hardware architecture adopting hierarchical input buffer to further reduce the pipeline stalls which cannot be handled by the RCSC format alone. The simulation results for various datasets show that combined use of the RSCS format and the proposed hardware requires 2x smaller inference runtime on average compared to the previous work.11Nsciescopu
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