김상범 교수

While naive Bayes is quite effective in various data mining tasks, it shows a disappointing result in the automatic text classification problem. Based on the observation of naive Bayes for the natural language text, we found a serious problem in the parameter estimation process, which causes poor results in text classification domain. In this paper, we propose two empirical heuristics: per-document text normalization and feature weighting method. While these are somewhat ad hoc methods, our prop

We demonstrate a neuromorphic core with 64k-cell phase change memory (PCM) synaptic array (256 axons by 256 dendrites) with in-situ learning capability. 256 configurable on-chip neuron circuits perform leaky integrate and fire (LIF) and synaptic weight update based on spike-timing dependent plasticity (STDP). 2T-1R PCM unit cell design separates LIF and STDP learning paths, minimizing neuron circuit size. The circuit implementation of STDP learning algorithm along with 2T-1R structure enables bo

The expeditious development of information technology has led to the rise of artificial intelligence (AI). However, conventional computing systems are prone to volatility, high power consumption, and even delay between the processor and memory, which is referred to as the von Neumann bottleneck, in implementing AI. To address these issues, memristor-based neuromorphic computing systems inspired by the human brain have been proposed. A memristor can store numerous values by changing its resistanc

Information retrieval using word senses is emerging as a good research challenge on semantic information retrieval. In this paper, we propose a new method using word senses in information retrieval: root sense tagging method. This method assigns coarse-grained word senses defined in WordNet to query terms and document terms by unsupervised way using co-occurrence information constructed automatically. Our sense tagger is crude, but performs consistent disambiguation by considering only the singl

Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low-impedance sensing are desired. However, it has been a struggle to find materials and fabrication methods that satisfy both of these properties simultaneously in a single substrate. Here, nanofiber channels with a self-formed ion-blocking layer ar

We study the drift behavior of RESET resistance <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$R_{\rm RESET}$</tex> </formula> and threshold switching voltage <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$V_{\rm th}$</tex></formula> in phase-change memory (PCM) and their temperature dependence. To extend the t

Discovering and understanding new materials with desired properties are at the heart of materials science research, and machine learning (ML) has recently offered special shortcuts to the ultimate goal. Thanks to the nourishment of computer hardware and computational chemistry, the development of calculated scientific data repositories could fuel the ML models to investigate the vast materials space. At this moment, understanding this revolutionary paradigm is urgent, and this Review aims to del

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We analyze constant-voltage isotropic and nonisotropic scaling issues for phase change memory (PCM) based on electrothermal physics. Various analytical and simulation models of general and typical PCM cells that support the analysis is also provided. The analysis shows that the maximum temperature in the PCM cell, which is a key parameter for PCM operation, is independent of geometrical sizes and dep

Abstract Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting considerable attention from academia and the industry. Although it is not completely successful yet, remarkable achievements have been reported pertaining to synaptic devices that can leverage NVM capable of storing multiple states. The analog synaptic devices performing computation similar to biological nerve systems are crucial in energy‐efficient analog neuromorphic computing systems. To use NVM as an analog sy

Abstract Cross‐point arrays of synaptic devices have been investigated as a core platform for neuromorphic computing architectures. To achieve a significant speed boost in deep neural network computations compared to the von Neumann architecture, it is essential to develop synaptic devices with optimal performance for fully parallel vector‐matrix‐multiplication. Among various non‐volatile memory candidates, metal‐oxide based electrochemical random‐access memory (ECRAM) is considered as a promisi

We demonstrate a novel confined PCM cell structure which utilizes a metallic surfactant layer to stabilize the high (and intermediate) resistance state drift in MLC phase change memory technology. The metallic surfactant layer provides an alternative conductive path to the amorphous region during read operation, which makes the cell characteristics immune to amorphous region instabilities such as time- and temperature-dependent resistance drift and noise. The data here focuses on time-dependent