Deploying Retrieval-Augmented Generation (RAG) on edge devices is in high demand, but is hindered by the latency of massive data movement and computation on traditional architectures. Compute-in-Memory (CiM) architectures address this bottleneck by performing vector search directly within their crossbar structure. However, CiM's adoption for RAG is limited by a fundamental ``representation gap,'' as high-precision, high-dimension embeddings are incompatible with CiM's low-precision, low-dimension array constraints. This gap is compounded by the diversity of CiM implementations (e.g., SRAM, ReRAM, FeFET), each with unique designs (e.g., 2-bit cells, 512x512 arrays). Consequently, RAG data must be naively reshaped to fit each target implementation. Current data shaping methods handle dimension and precision disjointly, which degrades data fidelity. This not only negates the advantages of CiM for RAG but also confuses hardware designers, making it unclear if a failure is due to the circuit design or the degraded input data. As a result, CiM adoption remains limited. In this paper, we introduce CQ-CiM, a unified, hardware-aware data shaping framework that jointly learns Compression and Quantization to produce CiM-compatible low-bit embeddings for diverse CiM designs. To the best of our knowledge, this is the first work to shape data for comprehensive CiM usage on RAG.
TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
Singapore University of Technology and Design（新加坡科技设计大学）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；A*STAR Institute of High Performance Computing（新加坡国立高级研究院高性能计算研究所）
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
Stanford University（斯坦福大学）；University of California San Diego（加利福尼亚大学圣地亚哥分校）；Tsinghua University（清华大学）；University of Notre Dame（圣母大学）；University of Pittsburgh（匹兹堡大学）
NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AI
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Univ. Grenoble Alpes, CEA, CNRS, Grenoble INP, and IRIG-Spintec（格勒诺布尔阿尔卑斯大学，法国原子能委员会，法国国家科学研究中心，格勒诺布尔国立理工学院，IRIG-Spintec）
Design of Memristive Lightweight Encryption For In-Memory Image Steganography
University of Isfahan（伊斯法罕大学）；Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University（伊斯兰自由大学谢赫雷分校）；AmirKabir University of Technology（阿米尔卡比尔理工大学）
