# Developing a Self-Explanatory Transformer

arXiv: https://arxiv.org/abs/2410.23083 · HTML: https://mlsanity.com/paper/2410.23083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Machine Learning-Based Graph Simplification for Symbolic Accelerators](https://mlsanity.com/paper/2605.08996/) (2026)

## Abstract (arXiv)

While IoT devices provide significant benefits, their rapid growth results in larger data volumes, increased complexity, and higher security risks. To manage these issues, techniques like encryption, compression, and mapping are used to process data efficiently and securely. General-purpose and AI platforms handle these tasks well, but mapping in natural language processing is often slowed by training times. This work explores a self-explanatory, training-free mapping transformer based on non-deterministic finite automata, designed for Field-Programmable Gate Arrays (FPGAs). Besides highlighting the advantages of this proposed approach in providing real-time, cost-effective processing and dataset-loading, we also address the challenges and considerations for enhancing the design in future iterations.