TY - GEN
T1 - Technological exploration of RRAM crossbar array for matrix-vector multiplication
AU - Gu, Peng
AU - Li, Boxun
AU - Tang, Tianqi
AU - Yu, Shimeng
AU - Cao, Yu
AU - Wang, Yu
AU - Yang, Huazhong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/3/11
Y1 - 2015/3/11
N2 - The matrix-vector multiplication is the key operation for many computationally intensive algorithms. In recent years, the emerging metal oxide resistive switching random access memory (RRAM) device and RRAM crossbar array have demonstrated a promising hardware realization of the analog matrix-vector multiplication with ultra-high energy efficiency. In this paper, we analyze the impact of nonlinear voltage-current relationship of RRAM devices and the interconnect resistance as well as other crossbar array parameters on the circuit performance and present a design guide. On top of that, we propose a technological exploration flow for device parameter configuration to overcome the impact of nonideal factors and achieve a better trade-off among performance, energy and reliability for each specific application. The simulation results of a support vector machine (SVM) and MNIST pattern recognition dataset show that the RRAM crossbar array-based SVM is robust to the input signal fluctuation but sensitive to the tunneling gap deviation. A further resistance resolution test presents that a 4-bit RRAM device is able to realize a recognition accuracy of ∼ 90%, indicating the physical feasibility of RRAM crossbar array-based SVM. In addition, the proposed technological exploration flow is able to achieve 10.98% improvement of recognition accuracy on the MNIST dataset and 26.4% energy savings compared with previous work.
AB - The matrix-vector multiplication is the key operation for many computationally intensive algorithms. In recent years, the emerging metal oxide resistive switching random access memory (RRAM) device and RRAM crossbar array have demonstrated a promising hardware realization of the analog matrix-vector multiplication with ultra-high energy efficiency. In this paper, we analyze the impact of nonlinear voltage-current relationship of RRAM devices and the interconnect resistance as well as other crossbar array parameters on the circuit performance and present a design guide. On top of that, we propose a technological exploration flow for device parameter configuration to overcome the impact of nonideal factors and achieve a better trade-off among performance, energy and reliability for each specific application. The simulation results of a support vector machine (SVM) and MNIST pattern recognition dataset show that the RRAM crossbar array-based SVM is robust to the input signal fluctuation but sensitive to the tunneling gap deviation. A further resistance resolution test presents that a 4-bit RRAM device is able to realize a recognition accuracy of ∼ 90%, indicating the physical feasibility of RRAM crossbar array-based SVM. In addition, the proposed technological exploration flow is able to achieve 10.98% improvement of recognition accuracy on the MNIST dataset and 26.4% energy savings compared with previous work.
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U2 - 10.1109/ASPDAC.2015.7058989
DO - 10.1109/ASPDAC.2015.7058989
M3 - Conference contribution
AN - SCOPUS:84926459123
T3 - 20th Asia and South Pacific Design Automation Conference, ASP-DAC 2015
SP - 106
EP - 111
BT - 20th Asia and South Pacific Design Automation Conference, ASP-DAC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 20th Asia and South Pacific Design Automation Conference, ASP-DAC 2015
Y2 - 19 January 2015 through 22 January 2015
ER -