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：电阻式交叉阵列实现的模拟存内计算（IMC）已成为深度神经网络（DNN）加速的一种有前景的架构，具有高存储带宽和原位计算能力。然而，手动、知识密集的设计过程以及高质量电路网表的缺失，严重限制了设计空间探索与优化至行为级系统工具层面。在本工作中，我们提出LIMCA——一种新型无需微调的大型语言模型（LLM）驱动框架，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采用无人工干预（NHIL）自动化流水线，生成并验证用于SPICE仿真的电路网表，消除了手动干预。通过利用结构化数据集和基于LLM的性能评估，LIMCA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成功生成了精度≥96%且功耗≤3W的交叉阵列设计，这成为LLM辅助IMC设计空间探索领域的首创工作。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，在确保用户约束的性能权衡的同时，缩短了设计探索时间。