Resistive crossbars enabling analog In-Memory Computing (IMC) have emerged as a promising architecture for Deep Neural Network (DNN) acceleration, offering high memory bandwidth and in-situ computation. However, the manual, knowledge-intensive design process and the lack of high-quality circuit netlists have significantly constrained design space exploration and optimization to behavioral system-level tools. In this work, we introduce LIMCA, a novel fine-tune-free Large Language Model (LLM)-driven framework for automating the design and evaluation of IMC crossbar architectures. Unlike traditional approaches, LIMCA employs a No-Human-In-Loop (NHIL) automated pipeline to generate and validate circuit netlists for SPICE simulations, eliminating manual intervention. LIMCA systematically explores the IMC design space by leveraging a structured dataset and LLM-based performance evaluation. Our experimental results on MNIST classification demonstrate that LIMCA successfully generates crossbar designs achieving $\geq$96% accuracy while maintaining a power consumption $\leq$3W, making this the first work in LLM-assisted IMC design space exploration. Compared to existing frameworks, LIMCA provides an automated, scalable, and hardware-aware solution, reducing design exploration time while ensuring user-constrained performance trade-offs.

翻译：支持模拟存内计算（IMC）的阻变交叉阵列已成为深度神经网络（DNN）加速的一种前景广阔的架构，能够提供高内存带宽和原位计算能力。然而，传统依赖人工、知识密集型的设计流程以及高质量电路网表的缺乏，极大地限制了设计空间探索与优化，使其主要停留在行为级系统工具层面。本文提出LIMCA，一种无需微调、由大语言模型（LLM）驱动的新型框架，用于自动化IMC交叉阵列架构的设计与评估。与传统方法不同，LIMCA采用“无人参与循环”（NHIL）的自动化流程来生成并验证用于SPICE仿真的电路网表，完全无需人工干预。LIMCA通过利用结构化数据集和基于LLM的性能评估，系统性地探索IMC设计空间。我们在MNIST分类任务上的实验结果表明，LIMCA成功生成的交叉阵列设计实现了准确率≥96%且功耗≤3W的性能，这是LLM辅助IMC设计空间探索领域的首次尝试。与现有框架相比，LIMCA提供了一种自动化、可扩展且硬件感知的解决方案，在确保满足用户约束的性能权衡的同时，显著缩短了设计探索时间。