AI in EE

AI IN DIVISIONS

AI in Circuit Division

AI in EE

AI IN DIVISIONS

AI in Circuit Division ​

AI in Circuit Division

A 36.2 dB High SNR and PVT/Leakage-robust eDRAM Computing-In-Memory Macro with Segmented BL and Reference Cell Array (유회준교수 연구실)

Computing-in-memory (CIM) shows high energy-efficiency through the analog DNN computation inside the memory macros. However, as the DNN size increases, the energy-efficiency of CIM is reduced by external memory access (EMA). One of the promising solutions is eDRAM based CIM to increase memory capacity with a high density cell. Although the eDRAM-CIM has a higher density than the SRAM-CIM, it suffers from both poor robustness and a low signal-to-noise ratio (SNR). In this work, the energy-efficient eDRAM-CIM macro is proposed while improving computational robustness and SNR with three key features: 1) High SNR voltage-based accumulation with segmented BL architecture (SBLA), resulting in 17.1 dB higher SNR, 2) canceling PVT/leakage-induced error with common-mode error canceling (CMEC) circuit, resulting in 51.4% PVT variation reduction and 51.4% refresh power reduction, 3) a ReLU-based zero-gating ADC (ZG-ADC), resulting in ADC power reduction up to 58.1%. According to these new features, the proposed eDRAM-CIM macro achieves 81.5-to-115.0 TOPS/W energy-efficiency with 209-to-295 μW power consumption when 4b×4b MAC operation is performed with 250 MHz core frequency. The proposed macro also achieves 91.52% accuracy at the CIFAR-10 object classification dataset (ResNet-20) without accuracy drop even with PVT variation.

Related papers:

Ha, Sangwoo, et al. “A 36.2 dB High SNR and PVT/Leakage-robust eDRAM Computing-In-Memory Macro with Segmented BL and Reference Cell Array.” IEEE Transactions on Circuits and Systems II: Express Briefs (2022).

Ha, Sangwoo, et al. “A 36.2 dB High SNR and PVT/Leakage-robust eDRAM Computing-In-Memory Macro with Segmented BL and Reference Cell Array”, IEEE International Symposium on Circuits and Systems (ISCAS), May. 2022