Sangheon Oh

发表

The Impact of Resistance Drift of Phase Change Memory (PCM) Synaptic Devices on Artificial Neural Network Performance

Zhisheng Huang, Duygu Kuzum, Sangheon Oh, 2019, IEEE Electron Device Letters.

A compact effective-current model for power performance analysis on state-of-the-art technology development and benchmarking

Sangheon Oh, Changhwan Shin, Wookhyun Kwon, 2015 .

Design Optimization for Process-Variation-Tolerant 22-nm FinFET-Based 6-T SRAM Cell with Worst-Case Sampling Method

Sangheon Oh, Changhwan Shin, 2016, IEICE Trans. Electron..

Drift-Enhanced Unsupervised Learning of Handwritten Digits in Spiking Neural Network With PCM Synapses

Duygu Kuzum, Sangheon Oh, Yuhan Shi, 2018, IEEE Electron Device Letters.

A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications

Sangheon Oh, Yuhan Shi, Xin Liu, 2019, Front. Neurosci..

Worst Case Sampling Method with Confidence Ellipse for Estimating the Impact of Random Variation on Static Random Access Memory (SRAM)

Sangheon Oh, Jung-Dong Park, Changhwan Shin, 2015 .

Performance Prospects of Deeply Scaled Spin-Transfer Torque Magnetic Random-Access Memory for In-Memory Computing

Xiao Lu, Duygu Kuzum, Sangheon Oh, 2020, IEEE Electron Device Letters.

Drift-enhanced Unsupervised Learning with PCM Synapses

Sangheon Oh, Yuhan Shi, Duygu Kuzum, 2018, 2018 76th Device Research Conference (DRC).

Adaptive Quantization as a Device-Algorithm Co-Design Approach to Improve the Performance of In-Memory Unsupervised Learning With SNNs

Zhisheng Huang, Duygu Kuzum, Sangheon Oh, 2019, IEEE Transactions on Electron Devices.

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays

Sangheon Oh, Yuhan Shi, Duygu Kuzum, 2018, Nature Communications.