Seongbin Oh

Vertical AND-Type Flash Synaptic Cell Stack for High-Density and Reliable Binary Neural Networks

Jangsaeng Kim, Jiseong Im, Seongbin Oh, Wonjun Shin et autres

A vertical AND-type (V-AND) cell stack consisting of flash memory cells is proposed and fabricated for hardware-based binary neural networks (BNNs). Low-power operation is possible with a semicircular poly-Si channel surrounded by a single word-line. In each floor, two cells facing each …

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Demonstration of In‐Memory Biosignal Analysis: Novel High‐Density and Low‐Power 3D Flash Memory Array for Arrhythmia Detection

Jangsaeng Kim, Jiseong Im, Wonjun Shin, Soochang Lee et autres

Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations …

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SNNSim: Investigation and Optimization of Large‐Scale Analog Spiking Neural Networks Based on Flash Memory Devices

Jonghyun Ko, Dongseok Kwon, Joon Hwang, Kyu‐Ho Lee et autres

Spiking neural networks (SNNs) have emerged as a novel approach for reducing computational costs by mimicking the biologically plausible operations of neurons and synapses. In this article, large‐scale analog SNNs are investigated and optimized at the hardware‐level by using SNNSim, the novel …

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Synaptic array using multi-level AND flash memory cells for hardware-based neural networks

Soochang Lee, Seongbin Oh, Sung Yun Woo, Byung‐Gook Park et autres

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Demonstration of integrate-and-fire neuron circuit for spiking neural networks

Sung Yun Woo, Won-Mook Kang, Young-Tak Seo, Soochang Lee et autres

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Synaptic Devices Based on 3D-Semicircular NAND Flash Memory

Seongbin Oh, Seungwhan Kim, Ho-Nam Yoo, Woo Young Choi et autres

Effects of Static Current in AND-Type TFT Synaptic Devices on Supervised On-Chip Training

Dongseok Kwon, Soochang Lee, Seongbin Oh, Chul-Heung Kim et autres

Neuron Circuits for Low-Power Spiking Neural Networks Using Time-To-First-Spike Encoding

Seongbin Oh, Dongseok Kwon, Gyuho Yeom, Won-Mook Kang et autres

Hardware-based Spiking Neural Networks (SNNs) are regarded as promising candidates for the cognitive computing system due to its low power consumption and highly parallel operation. In this paper, we train the SNN in which the firing time carries information using temporal backpropagation. …

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On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding

Jiseong Im, Jaehyeon Kim, Ho-Nam Yoo, Jong-Won Baek et autres

Artificial Neural Networks (ANNs) have shown remarkable performance in various fields. However, ANN relies on the von-Neumann architecture, which consumes a lot of power. Hardware-based spiking neural networks (SNNs) inspired by a human brain have become an alternative with significantly low power …

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Efficient fusion of spiking neural networks and FET-type gas sensors for a fast and reliable artificial olfactory system

Dongseok Kwon, Gyuweon Jung, Wonjun Shin, Yujeong Jeong et autres

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On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm

Jangsaeng Kim, Dongseok Kwon, Sung Yun Woo, Won-Mook Kang et autres

Abstract Reinforcement learning (RL) using deep Q-networks (DQNs) has shown performance beyond the human level in a number of complex problems. In addition, many studies have focused on bio-inspired hardware-based spiking neural networks (SNNs) given the capabilities of these technologies to realize …

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Spiking Neural Networks With Time-to-First-Spike Coding Using TFT-Type Synaptic Device Model

Seongbin Oh, Soochang Lee, Sung Yun Woo, Dongseok Kwon et autres

In hardware-based spiking neural networks (SNNs), the conversion of analog input data into the arrival time of an input pulse is regarded as a good candidate for the encoding method due to its bio-plausibility and power-efficiency. In this work, we trained an …

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