A Neural Net For a Graphing Calculator?

2025-07-17 · Source: neural network – Hackaday · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

ExploratoryStudios has developed the Hermes Optimus Neural Net for a TI-84 Plus Silver Edition, an autocorrect system designed to operate within the calculator's severe memory limitations. This neural network takes four-character inputs and matches them against a library of twelve target words, achieving approximately 85% accuracy. The system employs a feedforward neural network with a 4-60-12 architecture and sigmoid activation functions, running on a device with only 24 kB of RAM. The project highlights the engineering challenge of implementing machine learning on extremely resource-constrained hardware, rather than focusing on practical output. It also includes a confidence readout for its predictions.

Key takeaway

For AI Engineers exploring edge computing or embedded systems, this project demonstrates the feasibility and challenges of deploying neural networks on extremely limited hardware. You should consider how architectural choices, like the 4-60-12 feedforward design, can optimize for minimal RAM (e.g., 24 kB) and integrate confidence metrics, even for basic tasks, to enhance system interpretability and reliability in resource-scarce environments.

Key insights

Neural networks can be engineered for highly constrained environments like a TI-84 calculator.

Principles

• Memory constraints drive architectural innovation.
• Confidence readouts enhance system transparency.

Method

A feedforward neural network with a 4-60-12 architecture and sigmoid activation functions was implemented to perform autocorrection on a TI-84 Plus Silver Edition.

In practice

• Explore minimal neural net architectures.
• Integrate confidence scores in constrained ML.

Topics

• TI-84 Plus Silver Edition
• Neural Networks
• Memory Constraints
• Autocorrect System
• Feedforward Neural Network

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by neural network – Hackaday.