A 28-nm RRAM Computing-in-Memory Macro Using Weighted Hybrid 2T1R Cell Array and Reference Subtracting Sense Amplifier for AI Edge Inference

Non-volatile computing-in-memory (nvCIM) can potentially meet the ever-increasing demands on improving the energy efficiency (EF) for intelligent edge devices. However, it still suffers from limited input parallelism due to the parasitic effects, signal margin degradation due to device non-idealitie...

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Published in:IEEE journal of solid-state circuits 2023-10, Vol.58 (10), p.1-12
Main Authors: Ye, Wang, Wang, Linfang, Zhou, Zhidao, An, Junjie, Li, Weizeng, Gao, Hanghang, Li, Zhi, Yue, Jinshan, Hu, Hongyang, Xu, Xiaoxin, Yang, Jianguo, Liu, Jing, Shang, Dashan, Zhang, Feng, Tian, Jinghui, Dou, Chunmeng, Liu, Qi, Liu, Ming
Format: Article
Language:English
Subjects:
Analog readout
Arrays
Artificial intelligence
artificial intelligence (AI)
Computation
Computer memory
computing-in-memory
Costs
edge computing
Inference
Parallel processing
Performance evaluation
Resistance
resistive memory (RRAM)
sense amplifier (SA)
Sense amplifiers
Voltage
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Summary:Non-volatile computing-in-memory (nvCIM) can potentially meet the ever-increasing demands on improving the energy efficiency (EF) for intelligent edge devices. However, it still suffers from limited input parallelism due to the parasitic effects, signal margin degradation due to device non-idealities, and large hardware cost for analog readout. In this work, we present a two-transistor-one-resistor (2T1R) resistive memory (RRAM) nvCIM macro featuring: 1) a macro structure with decoupled memory and computing data paths; 2) the weighted hybrid 2T1R (WH-2T1R) cell array; 3) the redundant sub-array mapping scheme of the most-significant-bit (RSM-MSB); and 4) reference-subtracting current sense amplifier (RS-CSA). A test-chip is silicon-verified using the 28-nm high-k/metal-gate (HKMG) logic process with foundry-developed RRAM. The test-chip performs linear analog multiply-and-accumulate (MAC) operations over 32 accumulation channels and achieves 30.34-154.04 TOPS/W with 1-bit input (IN), 3-bit weight (W), and 4-bit output (O). Evaluations with the ResNet-18 model show that the MSB-RSM scheme results in 0.96% and 2.83% improvement on CIFAR-10 and CIFAR-100 inference accuracy, respectively.
ISSN:0018-9200
1558-173X
DOI:10.1109/JSSC.2023.3280357