LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs
Md Hafizur Rahman Prabuddha Chakraborty
Building efficient neural network architectures can be a time-consuming task
requiring extensive expert knowledge. This task becomes particularly
challenging for edge devices because one has to consider parameters such as
power consumption during inferencing, model size, inferencing speed, and CO2
emissions. In this article, we introduce a novel framework designed to
automatically discover new neural network architectures based on user-defined
parameters, an expert system, and an LLM trained on a large amount of
open-domain knowledge. The introduced framework (LeMo-NADe) is tailored to be
used by non-AI experts, does not require a predetermined neural architecture
search space, and considers a large set of edge device-specific parameters. We
implement and validate this proposed neural architecture discovery framework
using CIFAR-10, CIFAR-100, and ImageNet16-120 datasets while using GPT-4 Turbo
and Gemini as the LLM component. We observe that the proposed framework can
rapidly (within hours) discover intricate neural network models that perform
extremely well across a diverse set of application settings defined by the
user.