In-MRAM Computing Based on Complementary-Sensing Time-Based Readout Circuit Using Hybrid VGSOT-MTJ/GAA-CNTFET

作者：Zhongzhen Tong, Sifan Sun, Kaili Zhang, Chenghang Li, Daming Zhou, Zhaohao Wang, Xiaoyang Lin, Weisheng Zhao · 发表于：IEEE Transactions on Circuits & Systems II Express Briefs · 年份：2024 · DOI：10.1109/tcsii.2024.3460169 · 被引用次数：23 · 研究领域：Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices、Analytical Chemistry and Sensors

Gate-all-around carbon nanotube field-effect-transistors (GAA-CNTFETs) and voltage-gated spin-orbit torque magnetic tunnel junctions (VGSOT-MTJs) are expected to realize significant savings in energy consumption and computing delay compared to the existing silicon-based FinFETs. This brief proposes an in-MRAM computing macro based on a newly developed complementary-sensing time-based readout circuit (CSTRC) to accelerate binary neural networks (BNNs). An 8 kb MRAM was simulated using both GAA-CNTFET/VGSOT-MTJ and 14 nm FinFET/VGSOT-MTJ technologies to validate the effectiveness of the proposed design. The proposed CSTRC can achieve read operations and binary multiply-and-accumulate (BMAC) without additional peripheral circuits and achieve a notable decrease in the read bit error rate and column-level conditional row error rate by 1–5 and 1–13 orders of magnitude, respectively, compared to those reported previously. Moreover, under the GAA-CNTFET/VGSOT-MTJ process, the read energy consumption and delay were reduced by 59.1–78.9% and 23.9–29.7%, respectively; the BMAC energy efficiency and throughput were 10231 1-b TOPS/W and 1.8 TOPS, respectively increased by 2.9 and 1.27 times at 0.8 V supply voltage when comparing to its 14-nm FinFET /VGSOT-MTJ counterparts.