GSLP-CIM: A 28-nm Globally Systolic and Locally Parallel CNN/Transformer Accelerator With Scalable and Reconfigurable eDRAM Compute-in-Memory Macro for Flexible Dataflow
Yi Zhan, Wei-Han Yu, Ka-Fai Un, Rui Paulo Martins, Pui‐In Mak · IEEE Transactions on Circuits and Systems I Regular Papers · 2024
This article reports a globally systolic and locally parallel (GSLP) convolutional NN (CNN) and Transformer accelerator based on the scalable and reconfigurable (SR) embedded dynamic random-access memory (eDRAM) compute-in-memory (CIM) macro. It features: 1) a GSLP architecture employs systolic CIM macros with the reconfigurable inter-CIM network to support flexible dataflow, including weight stationary (WS), output stationary (OS), and Row stationary (RS); 2) an SR-CIM macro features reconfigurable weight/input/output memory ratio to maximize the related data reuse in different dataflow; 3) a high-density 3T eDRAM-CIM cell to further improve the density of the accelerator; 4) an area-efficient in-memory accumulator (IMA) to save the area and power overhead of the digital accumulation in each CIM macro. Prototyped in 28-nm CMOS process, the proposed GSLP-CIM accelerator exhibits a 4b peak throughput density of 0.16 TOPS/mm2 and a 4b peak compute energy efficiency of 3.55 TOPS/W. Specifically, evaluated with ResNet-50@ImageNet and ViT-B@ImageNet, this work reaches the system throughput of 24.5 and 5.66 inferences per second (IPS), the system throughput density of 19.3 IPS/mm2 and 4.46 IPS/mm2, the system compute energy efficiency of 423.9 inferences per watt (IPW) and 97.6 IPW, respectively.