3D-Domino: Ultra-Dense High-Accuracy 3D eDRAM-ROM Compute-In-Memory Based on CAA-IGZO TFT for Edge Large-Scale Model Inference

The rapid growth in the parameter count of large language models (LLMs) in recent years has placed higher demands on the density of compute-in-memory (CiM) solutions. Read-Only memory (ROM), due to its high-density advantages, has emerged as a promising CiM cell type, offering substantial task-level energy efficiency improvements over SRAM CiM. However, traditional 2D ROM CiM approaches are limited by 2D fabrication constraints, restricting scalability for LLM deployment. To address this limitation, this work explores a novel 3D back-end-of-line (BEOL)-compatible device, the channel-all-around (CAA)-IGZO TFT. Here, we propose a 3D ROM CiM with an ultra-dense cell structure and a high-throughput computing scheme. Additionally, we introduce a hybrid 3D CiM accelerator architecture that integrates both ROM and eDRAM for unprecedented density and flexibility. Evaluation results show that the proposed 3D ROM CiM, with 16 CAA-IGZO stacked layers, achieves an ultra-high memory density of 31.19 Mb/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>/layer, a computation density of 167.6 TOPS/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, and high computing accuracy with a compute SNR (CSNR) of 22.6 dB, underscoring its potential for edge large-scale model acceleration. Based on this, when deployed with a LoRA-tuned GPT-2 model, the proposed hybrid 3D eDRAM-ROM architecture shows 1.7× improvement in area efficiency compared to the eDRAM-only counterpart.