Using An Agent Based Model To Measure Industry Regulations: A Case of Online Games In Korea

As the online game industry in Korea exceeds $10 billion, social concerns of the industry in terms of policies and regulation areas are increasing. Specifically an enforcement ordinance activated by Korean government in the year of 2014 is told to result in reduced activities in the industry. However, quantitative approaches have been limited to understanding the post effectiveness of regulations. The lack of prior simulation efforts to game industry regulations yields to misunderstanding on optimal level for ruling out side effects. In this study, we suggest the application of agent based model or ABM as a smart service to measure industry policy effects. We review cases applying ABM in various domains with considering the possibility of using ABM in understanding the effectiveness of web board game regulations. We find that the ABM approach would be useful in areas such as analysis of regulatory effects that reflect a variety of characteristics and measurement of micro-regulatory effects and simulation of regulations. Considering the usefulness of ABM comprehensively, we proposes new directions including establishing a proactive measurement procedure of regulatory effectiveness and procedural a data-based quantitative analysis

Valley Degeneracies in (111) Silicon Quantum Wells

(111) Silicon quantum wells have been studied extensively, yet no convincing explanation exists for the experimentally observed breaking of 6 fold valley degeneracy into 2 and 4 fold degeneracies. Here, systematic sp3d5s* tight-binding and effective mass calculations are presented to show that a typical miscut modulates the energy levels which leads to breaking of 6 fold valley degeneracy into 2 lower and 4 raised valleys. An effective mass based valley-projection model is used to determine the directions of valley-minima in tight-binding calculations of large supercells. Tight-binding calculations are in better agreement with experiments compared to effective mass calculations.Comment: 4 pages, 3 figures, to appear in Applied Physics Letter

A second-order non-local model for granular flows

We determine a constitutive equation for developed three-dimensional granular flows based on a series of discrete element method simulations. In order to capture non-local phenomena, normal stress differences, and secondary flows, we extend a previously proposed granular temperature-sensitive rheological model by considering Rivlin-Ericksen tensors up to second order. Three model parameters are calibrated with the inertial number and a dimensionless granular temperature. We validate our model by running finite difference method simulations of inclined chute flows. The model successfully predicts the velocity and stress fields in this geometry, including secondary vortical flows that previous first-order models could not predict and slow creeping zones that local models miss. It simultaneously captures the non-trivial variation among diagonal components of the stress tensor throughout the domain

DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation

Transformer is a deep learning language model widely used for natural language processing (NLP) services in datacenters. Among transformer models, Generative Pre-trained Transformer (GPT) has achieved remarkable performance in text generation, or natural language generation (NLG), which needs the processing of a large input context in the summarization stage, followed by the generation stage that produces a single word at a time. The conventional platforms such as GPU are specialized for the parallel processing of large inputs in the summarization stage, but their performance significantly degrades in the generation stage due to its sequential characteristic. Therefore, an efficient hardware platform is required to address the high latency caused by the sequential characteristic of text generation. In this paper, we present DFX, a multi-FPGA acceleration appliance that executes GPT-2 model inference end-to-end with low latency and high throughput in both summarization and generation stages. DFX uses model parallelism and optimized dataflow that is model-and-hardware-aware for fast simultaneous workload execution among devices. Its compute cores operate on custom instructions and provide GPT-2 operations end-to-end. We implement the proposed hardware architecture on four Xilinx Alveo U280 FPGAs and utilize all of the channels of the high bandwidth memory (HBM) and the maximum number of compute resources for high hardware efficiency. DFX achieves 5.58x speedup and 3.99x energy efficiency over four NVIDIA V100 GPUs on the modern GPT-2 model. DFX is also 8.21x more cost-effective than the GPU appliance, suggesting that it is a promising solution for text generation workloads in cloud datacenters.Comment: Extension of HOTCHIPS 2022 and accepted in MICRO 202

Red-green-blue light sensitivity of oxide nanowire transistors for transparent display applications

In this study, the sensitivity of oxide nanowire transistors under red (R, 470 nm), green (G, 530 nm), and blue (B, 625 nm) light illumination was investigated. As the wavelength of light illuminating the nanowire channel region became shorter, a negative shift of threshold voltage, degradation of subthreshold slope, and increase of on-current were observed. This phenomenon can be explained in terms of photoinduced holes, creating interfacial traps between the gate dielectric and nanowire channel or reacting with oxygen ions on the surface of the nanowires. Thus, the attempt to minimize characteristic changes due to all RGB light sources was performed by employing ultraviolet-ozone treatment and passivation process. As a result, we could successfully fabricate oxide nanowire transistors providing high optical reliability which has broadened the possibilities for applying it to transparent and/or flexible pixel operation circuitry for displays with high optical reliability. Copyright 2013 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. [http://dx.doi.org/10.1063/1.4789405