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.

翻译：构建高效的神经网络架构通常是一项耗时且需要大量专家知识的工作。对于边缘设备而言，这一任务尤为具有挑战性，因为必须考虑推理功耗、模型大小、推理速度以及二氧化碳排放等多重参数。本文提出了一种新颖框架，能够基于用户自定义参数、专家系统以及在大规模开放领域知识上训练的大语言模型，自动发现新的神经网络架构。该框架（LeMo-NADe）专为非AI专家设计，无需预设神经架构搜索空间，并兼顾大量边缘设备特定参数。我们使用CIFAR-10、CIFAR-100和ImageNet16-120数据集，并以GPT-4 Turbo和Gemini作为大语言模型组件，对所提出的神经架构发现框架进行了实现与验证。实验表明，该框架能在数小时内快速发现复杂的神经网络模型，这些模型在用户定义的各种应用场景下均表现出色。