Chattronics: using GPTs to assist in the design of data acquisition systems

TL;DR

This paper presents Chattronics, a GPT-based tool that assists in designing data acquisition systems by generating architectural diagrams and specifications, demonstrating potential but facing technological limitations in handling complex requirements.

Contribution

Introduces a novel GPT-powered application for aiding the design of data acquisition systems using a Top-Down approach, with experimental validation on multiple projects.

Findings

GPTs can generate coherent system architectures.

The tool shows potential as an assistant in system design.

Technological limitations remain in handling complex requirements.

Abstract

The usefulness of Large Language Models (LLM) is being continuously tested in various fields. However, their intrinsic linguistic characteristic is still one of the limiting factors when applying these models to exact sciences. In this article, a novel approach to use General Pre-Trained Transformers to assist in the design phase of data acquisition systems will be presented. The solution is packaged in the form of an application that retains the conversational aspects of LLMs, in such a manner that the user must provide details on the desired project in order for the model to draft both a system-level architectural diagram and the block-level specifications, following a Top-Down methodology based on restrictions. To test this tool, two distinct user emulations were used, one of which uses an additional GPT model. In total, 4 different data acquisition projects were used in the testing phase, each with its own measurement requirements: angular position, temperature, acceleration and a fourth project with both pressure and superficial temperature measurements. After 160 test iterations, the study concludes that there is potential for these models to serve adequately as synthesis/assistant tools for data acquisition systems, but there are still technological limitations. The results show coherent architectures and topologies, but that GPTs have difficulties in simultaneously considering all requirements and many times commits theoretical mistakes.