A 22nm 41.8TFLOPS/W AI-Edge Transformer/CNN Nonvolatile-Processor Using QKV-Softmax-Layer-Fused Hybrid ReRAM-CIM and Concurrent-Transpose/Non-Transpose SRAM-CIM

Tiny AI-edge devices use nvCIM for power-off weight storage and active-mode computation, enabling high energy efficiency (EF) and low power-on latency. While tiny Transformer models offer higher accuracy than CNNs, they pose significant challenges for homogeneous nvCIM architectures due to attention-based operations that require dynamic matrix multiplications (MM), where both inputs and weights are generated on-the-fly. This paper presents a hybrid ReRAM-CIM and SRAM-CIM nvProcessor optimized for Transformer and CNN models, featuring a CIM-friendly layerfused flow. Fabricated in a 22 nm process, the proposed nvProcessor achieves 41.8 TFLOPS/W (system), 55.2 TFLOPS/W (macro) and 74.48% ImageNet accuracy, enabling high EF and accuracy with tiny (Mb-level) models.