Gao, Bin

Fully hardware-implemented memristor convolutional neural network

Yao, P., Wu, H., Gao, B., Tang, J., Zhang, Q., Zhang, W., Yang, J. J., & Qian, H. (2020) · Nature

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 2,179

A compute-in-memory chip based on resistive random-access memory

Wan, W., Kubendran, R., Schaefer, C., Eryilmaz, S. B., Zhang, W., Wu, D., Deiss, S., Raina, P., Qian, H., Gao, B., et al. (2022) · Nature

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 943

Face classification using electronic synapses

Yao, P., Wu, H., Gao, B., Eryilmaz, S. B., Huang, X., Zhang, W., Zhang, Q., Deng, N., Shi, L., Wong, H.-S. P., et al. (2017) · Nature Communications

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 909

Neuro-inspired computing chips

Zhang, W., Gao, B., Tang, J., Yao, P., Yu, S., Chang, M.-F., Yoo, H.-J., Qian, H., & Wu, H. (2020) · Nature Electronics

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 892

Reliability of analog resistive switching memory for neuromorphic computing

Zhao, M., Gao, B., Tang, J., Qian, H., & Wu, H. (2020) · Applied Physics Reviews

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 349

A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing

Zhong, Y., Tang, J., Li, X., Liang, X., Liu, Z., Li, Y., Xi, Y., Yao, P., Hao, Z., Gao, B., et al. (2022) · Nature Electronics

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 343

33.2 A Fully Integrated Analog ReRAM Based 78.4TOPS/W Compute-In-Memory Chip with Fully Parallel MAC Computing

Liu, Q., Gao, B., Yao, P., Wu, D., Chen, J., Pang, Y., Zhang, W., Liao, Y., Xue, C.-X., Chen, W.-H., et al. (2020) · 2020 IEEE International Solid- State Circuits Conference - (ISSCC)

Advanced Memory and Neural ComputingAnalog and Mixed-Signal Circuit Design

Cited by 308

Improving Analog Switching in HfO x -Based Resistive Memory With a Thermal Enhanced Layer

Wu, W., Wu, H., Gao, B., Deng, N., Yu, S., & Qian, H. (2017) · IEEE Electron Device Letters

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 282

Alloying conducting channels for reliable neuromorphic computing

Yeon, H., Lin, P., Choi, C., Tan, S. H., Park, Y., Lee, D., Lee, J., Xu, F., Gao, B., Wu, H., et al. (2020) · Nature Nanotechnology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 262

Binary neural network with 16 Mb RRAM macro chip for classification and online training

Yu, S., Li, Z., Chen, P.-Y., Wu, H., Gao, B., Wang, D., Wu, W., & Qian, H. (2016) · 2016 IEEE International Electron Devices Meeting (IEDM)

Advanced Memory and Neural ComputingAdvanced Neural Network Applications

Cited by 219

A neuromorphic visual system using RRAM synaptic devices with Sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling

Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H.-S. P. (2012) · 2012 International Electron Devices Meeting

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 213

An in-memory computing architecture based on a duplex two-dimensional material structure for in situ machine learning

Ning, H., Yu, Z., Zhang, Q., Wen, H., Gao, B., Mao, Y., Li, Y., Zhou, Y., Zhou, Y., Chen, J., et al. (2023) · Nature Nanotechnology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 198

A Methodology to Improve Linearity of Analog RRAM for Neuromorphic Computing

Wu, W., Wu, H., Gao, B., Yao, P., Zhang, X., Peng, X., Yu, S., & Qian, H. (2018) · 2018 IEEE Symposium on VLSI Technology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 197

Ultra-Low-Energy Three-Dimensional Oxide-Based Electronic Synapses for Implementation of Robust High-Accuracy Neuromorphic Computation Systems

Gao, B., Bi, Y., Chen, H.-Y., Liu, R., Huang, P., Chen, B., Liu, L., Liu, X., Yu, S., Wong, H.-S. P., et al. (2014) · ACS Nano

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 188

RRAM Crossbar Array With Cell Selection Device: A Device and Circuit Interaction Study

Deng, Y., Huang, P., Chen, B., Yang, X., Gao, B., Wang, J., Zeng, L., Du, G., Kang, J., & Liu, X. (2013) · IEEE Transactions on Electron Devices

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 184

HfOx based vertical resistive random access memory for cost-effective 3D cross-point architecture without cell selector

Chen, H.-Y., Yu, S., Gao, B., Huang, P., Kang, J., & Wong, H.-S. P. (2012) · 2012 International Electron Devices Meeting

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 144

Three-Dimensional nand Flash for Vector–Matrix Multiplication

Wang, P., Xu, F., Wang, B., Gao, B., Wu, H., Qian, H., & Yu, S. (2019) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 132

Investigation of statistical retention of filamentary analog RRAM for neuromophic computing

Zhao, M., Wu, H., Gao, B., Zhang, Q., Wu, W., Wang, S., Xi, Y., Wu, D., Deng, N., Yu, S., et al. (2017) · 2017 IEEE International Electron Devices Meeting (IEDM)

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 95

Design and optimization methodology for 3D RRAM arrays

Deng, Y., Chen, H.-Y., Gao, B., Yu, S., Wu, S.-C., Zhao, L., Chen, B., Jiang, Z., Liu, X., Hou, T.-H., et al. (2013) · 2013 IEEE International Electron Devices Meeting

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 55

A Compact Model of Analog RRAM With Device and Array Nonideal Effects for Neuromorphic Systems

Liao, Y., Gao, B., Xu, F., Yao, P., Chen, J., Zhang, W., Tang, J., Wu, H., & Qian, H. (2020) · IEEE Transactions on Electron Devices

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 53

A Voltage-Mode Sensing Scheme with Differential-Row Weight Mapping for Energy-Efficient RRAM-Based In-Memory Computing

Wan, W., Kubendran, R., Gao, B., Joshi, S., Raina, P., Wu, H., Cauwenberghs, G., & Wong, H. S. P. (2020) · 2020 IEEE Symposium on VLSI Technology

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 42

Multiplication on the edge

Wu, H., Yao, P., Gao, B., & Qian, H. (2018) · Nature Electronics

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 27