LLM-IMC: Automating Analog In-Memory Computing Architecture Generation with Large Language Models

Deepak Vungarala, Md Hasibul Amin, Pietro Mercati, Arman Roohi, Ramtin Zand, Shaahin Angizi

Abstract: Resistive crossbars enabling analog In-Memory Computing (IMC) have garnered significant attention from academia and industry as a promising architecture for Deep Neural Network (DNN) acceleration, thanks to their high memory access bandwidth and in-situ computing capabilities. However, the knowledge-intensive hardware design process and the lack of high-quality circuit netlists have constrained design space exploration and optimization of analog IMC to behavioral system-level tools. In this one-page abstract, we introduce LLM-IMC, a novel fine-tune-free Large Language Model (LLM) framework, supported by a Python-based tool, designed for analog IMC SPICE code generation. LLM-IMC systematically addresses these limitations by automating the creation of diverse IMC simulation scripts, enabling efficient design space exploration through LLM-driven performance, and outlining an integration roadmap for hardware-oriented neuromorphic crossbar design flows.

External IDs:dblp:conf/fccm/VungaralaAMRZA25