Can Low-Rank Knowledge Distillation in LLMs be Useful for Microelectronic Reasoning?

TL;DR

This paper investigates the use of a large language model, Llama-2-7B, as a microelectronic reasoning tool in electronic design automation, exploring various adaptation methods including a novel low-rank knowledge distillation scheme.

Contribution

It introduces a new low-rank knowledge distillation method for adapting LLMs to microelectronic reasoning tasks and evaluates its effectiveness.

Findings

Llama-2-7B can serve as a microelectronic Q&A expert.

The low-rank knowledge distillation scheme improves reasoning capabilities.

Empirical results demonstrate the potential of LLMs in EDA applications.

Abstract

In this work, we present empirical results regarding the feasibility of using offline large language models (LLMs) in the context of electronic design automation (EDA). The goal is to investigate and evaluate a contemporary language model's (Llama-2-7B) ability to function as a microelectronic Q & A expert as well as its reasoning, and generation capabilities in solving microelectronic-related problems. Llama-2-7B was tested across a variety of adaptation methods, including introducing a novel low-rank knowledge distillation (LoRA-KD) scheme. Our experiments produce both qualitative and quantitative results.