김성룡 교수

Abstract I study how a credit crunch affects output price dynamics. I build a unique micro-level data set that combines scanner-level prices and quantities with producer information, including the producer’s banking relationships, inventory, and cash holdings. I exploit the Lehman Brothers failure as a quasi-experiment and find that the firms facing a negative credit supply shock decrease their output prices approximately 15% more than their unaffected counterparts. I hypothesize that such firms

Manycore chips are widely employed in high-performance computing and large-scale data analysis. However, the design of high-performance manycore chips is dominated by power and thermal constraints. In this respect, voltage-frequency island (VFI) is a promising design paradigm to create scalable energy-efficient platforms. By dynamically tailoring the voltage and frequency of each island, we can further improve the energy savings within given performance constraints. Inspired by the recent succes

Multiple voltage frequency island (VFI)-based designs can reduce the energy dissipation in multicore platforms by taking advantage of the varying nature of the application workloads. Indeed, the voltage/frequency (V/F) levels of the VFIs can be dynamically tailored by considering the workload-driven variations in the application. Traditionally, mesh-based networks-on-chip (NoCs) have been used in VFI-based systems; however, they have large latency and energy overheads due to the inherently long

In the emerging data-driven science paradigm, computing syStems ranging from IoT and mobile to manycores and datacenters play distinct roles. These systems need to be optimized for the objectives and constraints dictated by the needs of the application. In this paper, we describe how machine learning techniques can be leveraged to improve the computational-efficiency of hardware design optimization. This includes generic methodologies that are applicable for any hardware design space. As an exam

Multiple Voltage Frequency Island (VFI)-based designs can reduce the energy dissipation in multicore chips. Indeed, by tailoring the voltages and frequencies of each VFI domain, we can achieve significant energy savings subject to specific performance constraints. The achievable performance of VFI-based multicore platforms depends on the overall communication backbone, which relies predominantly on Networks-on-Chip (NoCs). Traditionally mesh-based NoCs have been used in VFI-based systems. Howeve

We develop a framework to analyze the impact of trade shocks on a range of labor market adjustment margins in economies with a large number of sectors and labor groups. We provide analytic results characterizing equilibria. We show that labor groups earning a greater share of wage income in sectors with relative price declines experience a relative increase in unemployment and nonparticipation and decrease in wages and welfare. Our framework provides a guide for quantitative and empirical invest

In recent years, multiple Voltage Frequency Island (VFI)-based designs have increasingly made their way into both commercial and research multicore platforms. On the other hand, the wireless Network-on-Chip (WiNoC) architecture has emerged as an energy-efficient and high bandwidth communication backbone for massively integrated multicore platforms. It becomes therefore possible to exploit the small-world effects induced by the wireless links of a WiNoC to achieve efficient inter-VFI data exchang

What are the welfare implications of trade shocks? We provide a sufficient statistic that measures changes in welfare, to a first-order approximation, taking into account adjustment in labor supply, in frictional unemployment, and in the sectors to which workers apply while allowing for arbitrary heterogeneity in worker productivity and nonpecuniary returns across sectors. We apply these insights to measure changes in welfare across commuting zones (CZs) in the U.S. between 2000-2007. We find th

White-light phase-shifting interferometry (WLPSI) is widely recognized as a standard method to measure shapes with high resolution over a long distance. In practical applications, WLPSI, however, is associated with some degree of ambiguity of its phase, which occurs due to a phase delay, which is the offset between the phase of the fringes and the fringe envelope peak position. In this paper, a new algorithm is proposed for the determination of a fringe order suitable for samples in which the ph

Magnetic resonance imaging (MRI) is a common technique to scan brains for strokes, tumors, and other abnormalities that cause forms of dementia. However, correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3 patients with Alzheimer's were misdiagnosed in 2019, an issue neural networks can rectify. The performance of these neural networks have been shown to be improved by applying quantum algorithms. This proposed novel neural network architecture uses a fully-connected

Traditional multicore designs, based on the Network-on-Chip (NoC) paradigm, suffer from high latency, significant power consumption and temperature hotspots as the system size scales up due to the inherent multi-hop nature of the communication fabric. NoCs have been shown to achieve increased performance by inserting long-range wired links following the principles of small-world graphs [1]. Design and optimization of multi- and many-core systems on chip (SoCs) that exploit small-world effects ha

Magnetic resonance imaging (MRI) is a common technique to scan brains for strokes, tumors, and other abnormalities that cause forms of dementia. However, correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3 patients with Alzheimer's were misdiagnosed in 2019, an issue neural networks can rectify. The performance of these neural networks have been shown to be improved by applying quantum algorithms. This proposed neural network architecture uses a pooling layer, which r

Tight collaboration between manycore system designers and machine-learning experts is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine-learning techniques.