유연성, 효율성, 확장성 높은 두뇌 시뮬레이션 가속기 설계

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2023. 8. 김장우.두뇌의 연산 능력을 이해하고 활용하기 위해 신경과학자들은 시간에 따른 상태 변화를 예측하는 두뇌 시뮬레이션에 의존한다. 두뇌에서 나타나는 다양한 행동 양식을 탐색하고 신경과학에 크게 기여하기 위해서, 신경과학자들은 대규모 시뮬레이션을 유연하고 효율적인 시뮬레이션 프레임워크를 필요로 한다. 하지만, 기존 프레임워크는 소프트웨어 기반으로 시뮬레이션하여 너무 느리고 비효율적이다. 하드웨어 기반 가속기들은 이와 같은 비효율성을 완화하지만, 시뮬레이션의 유연성이 낮고 매 시간 간격마다 필요로하는 고정적으로 발생하는 연산 및 동기화에 의해 최적의 시뮬레이션 환경을 제공하지 못한다. 본 학위논문은 유연성, 효율성, 확장성을 극대화한 두뇌 시뮬레이션 프레임워크를 위해 설계한 아키텍처인 FlexLearn, NeuroEngine, NeuroSync를 소개한다. 첫째로 FlexLearn은 17가지 대표적인 하위 학습 방법을 지원하는 회로를 설계하고, 하위 학습 방법을 조합하여 타겟하는 학습 모델을 시뮬레이션할 수 있게 만들었다. 다음 NeuroEngine은 매 시간 간격마다 뉴런의 상태 변화를 계산하지 않고 아닌 특정 이벤트가 발생하는 경우에만 상태 변화를 계산하여 시뮬레이션 효율성을 극대화한다. 마지막으로 NeuroSync는 매 시간 간격마다 시뮬레이션을 동기화하는 과정에서 발생하는 오버헤드를 최소화한다.To fully understand and utilize the compute capabilities of the brain, neuroscientists rely on brain simulations that incorporate the concept of time into their operating model. The simulation involves computing the neuronal state changes over time and communicating the spikes via synapses. It also simulates learning by evaluating how the synapses change their weights in response to the spiking activity of the neurons. To explore various behaviors of the brain and thus make great advances, researchers demand a simulation framework to support large-scale simulations in both a flexible and efficient manner. However, existing simulation frameworks are too slow and inefficient due to their software-based simulation methodology. The hardware-based accelerators have mitigated the performance overhead to some extent, but they are still sub-optimal due to their inflexible hardware designs and excessive computation and synchronization overhead at each time interval. In this dissertation, we introduce the FlexBrain project to design a flexible, efficient, and scalable brain simulation framework. In this project, we design three different hardware designs (i.e., FlexLearn, NeuroEngine, and NeuroSync) to meet the three goals in a step-by-step manner. First, FlexLearn achieves high flexibility by identifying and supporting 17 representative sub-rules, which can be combined to simulate target learning rules. Second, NeuroEngine enables event-driven computations of the neuronal state changes to greatly reduce the excessive computation overhead using its specialized datapath and memory units. Third, NeuroSync enables a scalable and accurate simulation by adopting speculative synchronization while supporting rollback and recovery mechanisms to ensure simulation accuracy. The three architectures can be seamlessly integrated to design FlexBrain simulator, which serves as an optimal brain simulation framework.1 Introduction 1 2 Background 6 2.1 Organization of the Brain 6 2.2 Modeling the Brain 7 2.3 Simulating the Brain 9 2.3.1 Single Core Simulation 9 2.3.2 Multicore Simulation 11 3 FlexLearn: Fast and Highly Efficient Brain Simulations Using Flexible On-Chip Learning 13 3.1 Motivation & Design Goals 13 3.1.1 High Computational Overheads 13 3.1.2 Limitations of the Existing Accelerators 15 3.1.3 Design Goals 16 3.2 FlexLearn Architecture 16 3.2.1 Target Learning (Sub-)Rules 17 3.2.2 Efficient Integration of the Datapaths 30 3.2.3 End-to-End Brain Simulation Processor 32 3.3 Evaluation 34 3.3.1 Experimental Setup 34 3.3.2 Flexible & Efficient Brain Simulations 36 3.3.3 Low Hardware Costs 38 4 NeuroEngine: A Hardware-Based Event-Driven Simulation System for Advanced Brain-Inspired Computing 40 4.1 Motivation 40 4.1.1 Categories of Neuron Models 40 4.1.2 Time-Driven & Event-Driven Simulation 42 4.2 Challenges 45 4.2.1 Complex Computations 45 4.2.2 Complex Scheduling 47 4.2.3 Resource Contention 47 4.3 NeuroEngine Architecture 48 4.3.1 Optimized Datapath 48 4.3.2 Composite Queue 50 4.3.3 Lazy Update 53 4.4 Implementation 58 4.4.1 NeuroEngine Hardware 58 4.4.2 Software Stack 59 4.5 Evaluation 61 4.5.1 Experimental Setup 61 4.5.2 Efficiency of the Hardware-based Solutions 62 4.5.3 Low Event-Driven Computation Costs 63 4.5.4 Eliminated Redundant Event Scheduling 65 4.5.5 Speedup Using Lazy Update 66 4.5.6 Fast & Efficient Event-Driven Simulations 66 4.5.7 Iso-area Comparisons 67 5 NeuroSync: A Scalable and Accurate Brain Simulator Using Safe and Efficient Speculation 69 5.1 Motivation 69 5.2 Key Ideas 70 5.2.1 Speculative Brain Simulation 72 5.2.2 Optimal Speculative Simulation 76 5.3 NeuroSync Architecture 80 5.3.1 Baseline Simulator 81 5.3.2 RR manager 82 5.3.3 On-Demand trace provider 83 5.3.4 Deferred Learning handler 84 5.4 Simulation Front-Ends 85 5.5 Evaluation 85 5.5.1 Experimental Setup 85 5.5.2 Results 87 6 A Flexible, Efficient, and Scalable Simulation System 93 7 RelatedWork 95 8 Conclusion 99 Abstract (In Korean) 120박

중모리를 중심으로

학위논문 (석사) -- 서울대학교 대학원 : 음악대학 음악과, 2021. 2. 김우진.This study aims to discover the differences and characteristics of the two sanjo by comparing and analyzing the jungmori of Seo Yong-Seok school daegeum-sanjo and Won Jang-Hyeon school daegeum-sanjo. The results of the study are as follows. First of all, the following is a comparison of the melody of the jungmori of Seo Yong-Seok school daegeum-sanjo and the jungmori of Won Jang-Hyeon school daegeum-sanjo. Both streams commonly have the biggest differential melody in the C-key gyemyeon-jo. The difference between the two streams is that Seo Yong-Seok school has a higher proportion of differential melody in the C-key gyemyeon-jo, when compared to Won Jang-Hyeon school, and Won Jang-Hyeon school has a higher proportion of differential melody in the B ♭-key pyeong-jo than Seo Yong-Seok school . Secondly, the following is a comparison of the musical tones of the jungmori of Seo Yong-Seok school daegeum-sanjo and the jungmori of Won Jang-Hyeon school daegeum-sanjo. The jungmori of Seo Yong-Seok school and the jungmori of Won Jang-Hyeon school uses identical musical tones of Nadereum-jo, pyeong-jo, and gyemyeon-jo, and the order of appearance is identical in the order of Nadereum-jo, pyeong-jo, and followed by gyemyeon-jo. Through this, it is identified that the jungmori of Seo Yong-Seok school has a high proportion of C-key gyemyeon-jo melody, and the jungmori of Won Jang-Hyeon school has a high proportion of B ♭-key pyeong-jo melody. Third, when the jang(章) of jungmori of Seo Yong-Seok school daegeum-sanjo and the jungmori of Won Jang-Hyeon school daegeum-sanjo are compared, it is as follows. The common factor between the jang(章) of Seo Yong-Seok school jungmori and Won Jang-Hyeon school jungmori is that there is a four-changdan basic type jang(章) of ‘Gi-gyeong-gyeol-hae’ type. The difference between the two is shown in the expanded type of jang(章). The Seo Yong-Seok school jungmori’s expanded type of jang(章) shows a 5 jangdan style jang(章) of ‘Gi-gyeong-gyeong-gyeol-hae’ type and ‘Gi-gyeong-gyeol-hae-hae’ type. On the other hand, the Won Jang-Hyeon schooljungmori’s expanded type of jang(章) shows a six-changdan type jang(章) of ‘Gi-gyeong-gyeong-gyeol-hae-hae’ type. Fourth, when the modulation and transposition of jungmori of Seo Yong-Seok school daegeum-sanjo and the jungmori of Won Jang-Hyeon school daegeum-sanjo are compared, it is as follows. In the doljang of Seo Yong-Seok school jungmori, the musical tone of C-key Nadereum-jo(u-jo) and C-key gyemyeon-jo appears, and in the temporary transposition, the musical tone of C-key gyemyeon-jo and F-key gyemyeon-jo appears. Meanwhile, in the doljang of Won Jang-Hyeon school jungmori, , the musical tone of B♭-key pyeong-joand C-key gyemyeon-jo appears, and in the temporary modulation, the musical tone of B♭-key pyeong-joand C-key gyemyeon-jo appears. Fifth, the following is a comparison of the breathing lines of the jungmori of Seo Yong-Seok school daegeum-sanjo and the jungmori of Won Jang-Hyeon school daegeum-sanjo. There are a total of 491 breathing lines in Seo Yong-Seok school jungmori, and a total of 257 in the Won Jang-Hyeon school. As can be seen, Seo Yong-Seok school has 234 more breathing lines. Among the changdan of the two streams, the number of breathing lines of changdan, which has the least number of breathing lines, are Seo Yong-Seok school jungmori with 4 lines and Won Jang-Hyeon school jungmori with 2 lines. On the contrary, among changdan of two streams, the number of breathing lines of changdan, which has the most number of breathing lines, are Seo Yong-Seok school jungmori with 14 lines and Won Jang-Hyeon school jungmori with 10 lines. Sixth, Won Jang-Hyeon school has more grace note among the two stream’s identical melody, when compared to Seo Yong-Seok school . Seo Yong-Seok school ’s identical melody has 10 grace notes, whereas Won Jang-Hyeon school has 29 of them. It is observed that Won Jang-Hyeon school plays more grace notes than Seo Yong-Seok school . Seventh, the following is a comparison of the breathing lines of identical melody of the jungmori of Seo Yong-Seok school daegeum-sanjo and the jungmori of Won Jang-Hyeon school daegeum-sanjo. Seo Yong-Seok school has 92 breathing lines of identical melody in total, whereas Won Jang-Hyeon school has 72. Seo Yong-Seok school has 20 more breathing lines. Based on Han Ju Hwan style daegeum-sanjo, Seo Yong-Seok school tends to increase its breathing lines, whereas Won Jang-Hyeon school tends to decrease its breathing lines. When Seo Yong-Seok school and Won Jang-Hyeon school are compared, it is seen that Seo Yong-Seok school plays with more breathing lines, whereas Won Jang-Hyeon school plays with less breathing lines. The biggest differences between Seo Yong-Seok school jungmori and Won Jang-Hyeon school jungmori are the breathing lines and grace notes. While Seo Yong-Seok school jungmori is characterized by many breathing lines and few grace notes, Won Jang-Hyeon school jungmori is characterized by less breathing lines and a lot of grace notes. The daegeum-sanjo has experienced expansion and change over a long period of time as it has been passed down in people’s daily life. During this process, the lives and musical activities of masters who created the streams of daegeum-sanjo melted into their music.본 논문에서는 서용석류 대금산조와 원장현류 대금산조의 중모리를 비교 및 분석하여 두 산조의 차이점과 특징들을 찾아내고자 하였다. 그 결과는 다음과 같다. 첫째, 서용석류 대금산조 중모리와 원장현류 대금산조 중모리의 선율을 비교하면 다음과 같다. 두 유파는 공통적으로 C청 계면조에서 가장 많은 상이선율이 나타난다. 두 유파의 차이점은 서용석류가 원장현류에 비해 C청 계면조에서의 상이선율 비중이 높으며, 원장현류가 서용석류에 비해 B♭청 평조의 상이선율이 비중이 높다는 것이다. 둘째, 서용석류 대금산조 중모리와 원장현류 대금산조 중모리의 악조를 비교하면 다음과 같다. 서용석류 중모리와 원장현류 중모리는 내드름조 · 평조 · 계면조의 악조가 동일하게 사용되며, 조의 출현 순서는 내드름조-평조-계면조 순으로 동일하게 구성되었다. 이를 통해 서용석류 중모리는 C청 계면조 선율의 비중이 높고, 원장현류 중모리는 B♭청 평조 선율의 비중이 높은 것을 확인할 수 있다. 셋째, 서용석류 대금산조 중모리와 원장현류 대금산조 중모리의 장(章)을 비교하면 다음과 같다. 서용석류 중모리 장(章)과 원장현류 중모리 장(章)의 공통점은 기-경-결-해 유형의 4장단형 기본형식 장(章)이 나타난다는 것이다. 두 유파의 차이점은 확대형식 장(章)에서 나타난다. 서용석류 중모리의 확대형식 장(章)에는 기-경-경-결-해와 기–경-결-해-해 유형의 5장단형 장(章)이 나타나는 것이 특징이다. 반면, 원장현류 중모리의 확대형식 장(章)에는 기-경-경-결-해-해 유형의 6장단형 장(章)이 나타나는 것이 특징이다. 넷째, 서용석류 대금산조 중모리와 원장현류 대금산조 중모리의 변조와 변청을 비교하면 다음과 같다. 서용석류 중모리의 돌장에는 C청 내드름조(우조)와 C청 계면조의 악조가 나타나고 일시적 변청에는 C청 계면조와 F청 계면조의 악조가 나타난다. 반면, 원장현류 중모리의 돌장에는 B♭청 평조와 C청 계면조의 악조가 나타나고 일시적 변조에도 B♭청 평조와 C청 계면조의 악조가 나타난다. 다섯째, 서용석류 대금산조 중모리와 원장현류 대금산조 중모리의 호흡선을 비교하면 다음과 같다. 서용석류 중모리의 호흡선은 총 491개이고, 원장현류의 총 257개로 서용석류가 234개가 더 많다. 두 유파의 장단 중, 가장 적은 수의 호흡선으로 구성된 장단의 호흡선 수는 서용석류 중모리가 4개, 원장현류 중모리가 2개이다. 반대로 두 유파의 장단 중, 가장 많은 수의 호흡선으로 구성된 장단의 호흡선 수는 서용석류 중모리가 14개, 원장현류 중모리가 10개이다. 여섯째, 서용석류 대금산조 중모리와 원장현류 대금산조 중모리의 동일선율 호흡선을 비교하면 다음과 같다. 서용석류의 동일선율 호흡선은 총 92개이고 원장현류는 72개이다. 서용석류 호흡선이 20개가 더 많다. 한주환류 대금산조를 기준으로 서용석류는 호흡선이 늘어나고, 원장현류는 호흡선이 줄어드는 경향이 나타난다. 서용석류가 원장현류와 비교했을 때 더 많은 호흡선으로 연주하고, 원장현류는 더 적은 호흡선으로 연주하는 것을 확인할 수 있다. 서용석류 중모리와 원장현류 중모리의 가장 큰 차이점은 호흡선과 꾸밈음이라 할 수 있다. 서용석류 중모리는 많은 호흡선과 적은 꾸밈음으로 연주된 특징이 있다면, 원장현류 중모리는 적은 호흡선과 많은 꾸밈음으로 연주된 것이 큰 특징이다.Ⅰ. 서 론 1 1. 연구목적 및 문제 제기 1 2. 선행연구 5 3. 연구방법 및 연구범위 12 4. 서용석과 원장현의 학습과정 20 Ⅱ. 서용석류 대금산조의 특징 24 1. 악조 24 2. 장(章) 32 3. 변청과 변조 41 2. 호흡선 44 Ⅲ. 원장현류 대금산조의 특징 55 1. 악조 55 2. 장(章) 62 3. 변청과 변조 70 4. 호흡선 75 Ⅳ. 서용석류 대금산조와 원장현류 대금산조 비교 84 1. 서용석류 중모리와 원장현류 중모리 선율 비교 84 2. 악조 비교 101 3. 장(章) 103 4. 변조와 변청 104 5. 호흡선 106 Ⅴ. 결 론 132 참고문헌 135 Abstract 140 부록 145Maste

Creative Destruction Mechanism of Korean Industry from the Perspective of Industrial Dynamics

학위논문 (박사)-- 서울대학교 대학원 공과대학 협동과정 기술경영·경제·정책전공, 2017. 8. 이정동.Schumpeter's study on economic growth and technological progress in the capitalist system as a process of creative destruction has influenced many studies on industry dynamics. In addition to Schumpeterianism, evolutionary economics and organizational ecology also attempted to grasp the sources of industrial dynamics. Although each perspective is slightly different, they all perceived competition in the market as a major source of industrial dynamics. In particular, Schumpeterianism emphasized Schumpeterian competition that firms are competing their competitive advantage originated from innovation as a main source of industry dynamics. In this perspective, this study attempted to analyze the creative destruction mechanism of Korean industry. In particular, we tried to describe the selection criteria exists in the Korean industry through empirical analysis of exit firms. First, we reviewed theoretical background and the empirical analysis on the survival of firms and derived stylized facts on firm survival. The stylized facts were classified into individual level, firm level, industry level, and macroeconomic level. At the individual level, it was possible to derive a stylized fact that the higher the level of education and experience of the organization members, the better the survival of the firm. At the firm level, the firm size, age, R&D investment, and exporting and were identified as significant determinants on the firm survival. At the industrial level, it was reported that the firm entry rate, industrial growth rate, which determine the degree of competition of the industry, and technology intensity as determinants on the survival of the firm. At the macroeconomic level, we were able to derive a stylized fact that firms' survival rate is procyclic to upturns and downturns of the economy. Second, survival analysis was implemented to describe the selection criteria of Korean industry through firm level micro data. The results showed that the stylized facts on the survival such a as firm size, age, and R&D investment is also found in Korean industry. In addition, we found the changes in the firm selection criteria as a result of restructuring of the financial sector and the industry sector in the process of overcoming the Asian financial crisis. More specifically, it was found that there was a change in firm financial management behavior before and overall incentive in terms of firm survival for the firm's investment activity was reduced after the Asian financial crisis. Third, we focused on the cleansing effect hypothesis in economic recessions. This study investigated two recessions in Korea, the Asian financial crisis and the global financial crisis. We measured total factor productivity using micro level manufacturing plant data from 1993 to 2013 and decomposed the source of the changes in total factor productivity to measure the cleansing effect in two large recessions. During the Asian financial crisis, there was no evidence to support a cleansing effect hypothesis. In contrast, during the global financial crisis, we found the evidence of a cleansing effect. Additionally, we found differences in market selection criteria in the two recessionsby the global financial crisis, the market selection criteria had changed to enable a more conducive environment for the creative destruction process. Fourth, the problem of zombie companies was investigated from a different perspective. Previous studies have recognized zombie companies as a factor that hinders the creative destruction process and recognized that they should be exited through restructuring. However, this study focuses on the fact that the problem of zombie firm may be different according to the financial system of the country. Specifically, we analyzed the characteristics of recovering firms and exiting firms in the credit based financial system such as Korea. Based on the firm level micro data, it was found that the firms with high amount of accumulated knowledge showed higher probability of recovering to the normal firms. Also, it is confirmed that the financial sector was not able to identify and support selectively firms between recovering firms and exiting firms.Introduction 1 Chapter 1. Research Background 5 1.1 Theoretical background on firm survival 5 1.1.1 Firm survival from the Schumpeterian perspective 6 1.1.2 Firm survival from an evolutionary perspective 8 1.1.3 Firm survival from an organizational ecology perspective 10 1.2 Empirical background on firm survival 12 1.2.1 Firm survival determinants: Individual level 13 1.2.2 Firm survival determinants: Firm level 15 1.2.3 Firm survival determinants: Industry level 26 1.2.4 Firm survival determinants: Macro level 31 1.3 Sub-conclusion 35 Chapter 2. Evolution of Firm Selection Criteria in the Korean Manufacturing Sector 37 2.1 Introduction 37 2.2 Literature review 39 2.3 Research hypothesis 45 2.4 Empirical strategy 50 2.4.1 Survival function and hazard function 50 2.4.2 Parametric survival analysis model 53 2.5 Data and Variables 56 2.5.1 Data 56 2.5.2 Variables 58 2.6 Results 60 2.6.1 Specification of parametric survival function 60 2.6.2 Regression result of parametric survival model 61 2.7 Sub-conclusion 66 Chapter 3. Productivity Dynamics and Cleansing Effect of Two Economic Crisis in Korean Manufacturing Sector 69 3.1 Introduction 69 3.2 Literature review 72 3.3 Research hypothesis 77 3.3.1 Cleansing effect in the Asian financial crisis 79 3.3.2 Cleansing effect in the global Financial Crisis 81 3.4 Empirical strategy 82 3.4.1 Measure of Total Factor Productivity 83 3.4.2 Decompose TFP growth 85 3.4.3 Survival analysis: Cox proportional hazard model 86 3.5 Data and variables 87 3.5.1 Data 87 3.5.2 Variables 91 3.6 Results 92 3.6.1 Calculation result of total factor productivity (TFP) 92 3.6.2 Decomposition analysis result of productivity growth 94 3.6.3 Survival analysis result: Cox proportional hazard model 98 3.7 Sub-conclusion 103 Chapter 4. Identifying the Real Zombie Firms: The Role of Finance 105 4.1 Introduction 105 4.2 Literature review 107 4.2.1 Zombie firms in previous literature 107 4.2.2 Role of finance and finance system 109 4.3 Research hypothesis 113 4.4 Empirical strategy 115 4.4.1 Competing risk model 115 4.4.2 Probit model 119 4.5 Data and variables 120 4.5.1 Data 120 4.5.2 Variables 125 4.6 Result 126 4.6.1 Regression result of competing risk model 126 4.6.2 Regression result of probit model 128 4.7 Sub-conclusion 130 Chapter 5. Conclusion 134 5.1 Summary of the study 134 5.2 Implications and limitations of study 137 Bibliography 141 Appendix 1: List of empirical studies on firm survival determinants covered in Chapter 1 159 Appendix 2: Descriptive statistics of empirical data in Chapter 2 166 Appendix 3: Descriptive statistics of empirical data in Chapter 3 171 Appendix 4: Result of aggregated productivity growth decomposition analysis by industry 172 Appendix 5: Descriptive statistics of empirical data in Chapter 4 175 Abstract (Korean) 178Docto