DIRC-RAG: Accelerating Edge RAG with Robust High-Density and High-Loading-Bandwidth Digital In-ReRAM Computation
Kunming Shao, Zhipeng Liao, Jiangnan Yu, Liang Zhao, Qiwei Li, Xijie Huang, Jingyu He, Fengshi Tian, Yi Zou, Xiaomeng Wang, Tim Kwang-Ting Cheng, Chi-Ying Tsui

TL;DR
DIRC-RAG introduces a high-density, low-power digital in-ReRAM computation architecture to accelerate retrieval-augmented generation on edge devices, significantly reducing latency and energy consumption while maintaining accuracy.
Contribution
It presents a novel DIRC architecture combining high-density ReRAM with digital MAC operations, enabling efficient, robust, and low-power edge RAG acceleration.
Findings
Achieves 5.18Mb/mm2 memory density with 131 TOPS throughput.
Reduces retrieval latency to 5.6μs/query and energy to 0.956μJ/query.
Maintains retrieval precision with error optimization and detection circuits.
Abstract
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval but faces challenges on edge devices due to high storage, energy, and latency demands. Computing-in-Memory (CIM) offers a promising solution by storing document embeddings in CIM macros and enabling in-situ parallel retrievals but is constrained by either low memory density or limited computational accuracy. To address these challenges, we present DIRCRAG, a novel edge RAG acceleration architecture leveraging Digital In-ReRAM Computation (DIRC). DIRC integrates a high-density multi-level ReRAM subarray with an SRAM cell, utilizing SRAM and differential sensing for robust ReRAM readout and digital multiply-accumulate (MAC) operations. By storing all document embeddings within the CIM macro, DIRC achieves ultra-low-power, single-cycle data loading, substantially reducing…
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