Abstract
Building efficient neural network architectures for a given dataset can be a time-consuming task requiring extensive expert knowledge. This task becomes particularly challenging for edge artificial intelligence (AI) because one has to consider additional parameters such as power consumption during inferencing, model size, and inferencing speed. 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 proposed framework (LEMONADE) can be easily used by non-AI experts, does not require a predetermined neural architecture search space, and considers a large set of edge AI parameters. We implement and validate this proposed neural architecture discovery framework using CIFAR-10, CIFAR-100, ImageNet16-120, EuroSAT, Malaria Parasite, and IMDb datasets while primarily using ChatGPT-4o as the LLM component. We have also explored the possibilities of using Gemini-Pro as the LLM component. Neural networks generated using LEMONADE for CIFAR-10 ([Formula: see text] test accuracy) and CIFAR-100 ([Formula: see text] test accuracy) demonstrated state-of-the-art performance in terms of final model accuracy. We have also observed near state-of-the-art performance (in terms of accuracy) for the ImageNet16-120 dataset. Moreover LEMONADE was able to generate effective neural networks, satisfying different edge AI requirements across additional datasets such as EuroSAT.