Nature Electronics ( IF 27.500 ) Pub Date : 2019-08-26 , DOI: 10.1038/s41928-019-0288-0 Wei-Hao Chen, Chunmeng Dou, Kai-Xiang Li, Wei-Yu Lin, Pin-Yi Li, Jian-Hao Huang, Jing-Hong Wang, Wei-Chen Wei, Cheng-Xin Xue, Yen-Cheng Chiu, Ya-Chin King, Chorng-Jung Lin, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea-Tiong Tang, J. Joshua Yang, Mon-Shu Ho, Meng-Fan Chang
Non-volatile computing-in-memory (nvCIM) could improve the energy efficiency of edge devices for artificial intelligence applications. The basic functionality of nvCIM has recently been demonstrated using small-capacity memristor crossbar arrays combined with peripheral readout circuits made from discrete components. However, the advantages of the approach in terms of energy efficiency and operating speeds, as well as its robustness against device variability and sneak currents, have yet to be demonstrated experimentally. Here, we report a fully integrated memristive nvCIM structure that offers high energy efficiency and low latency for Boolean logic and multiply-and-accumulation (MAC) operations. We fabricate a 1 Mb resistive random-access memory (ReRAM) nvCIM macro that integrates a one-transistor–one-resistor ReRAM array with control and readout circuits on the same chip using an established 65 nm foundry complementary metal–oxide–semiconductor (CMOS) process. The approach offers an access time of 4.9 ns for three-input Boolean logic operations, a MAC computing time of 14.8 ns and an energy efficiency of 16.95 tera operations per second per watt. Applied to a deep neural network using a split binary-input ternary-weighted model, the system can achieve an inference accuracy of 98.8% on the MNIST dataset.