2 research outputs found
Understanding Consumer Churning Behaviors in Mobile Telecommunication Service Industry : Cross-national Comparison between Korea and China
It is crucial for telecom operators to shift from a subsidy-centered to a service-centered marketing strategy. As the market reaches its saturation point, the effectiveness of customer acquisition through the traditional subsidy decreases and offering differentiated services for existing customer retention is critical. This study aims at finding out factors affecting consumer churning intention based on PPM (Push-Pull-Mooring) theory in order to exactly understand consumer behaviors in the mobile telecommunication service industry (MTSI). This study also analyzes the difference between Korea (saturation market) and China (growth market) in terms of the causality between the intention to switch and the affecting factors. The research findings will encourage telecom operators to establish the effective service strategy that corresponds to their level of market maturity
Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing
Abstract Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems