Developing a Self-Explanatory Transformer

TL;DR

This work explores a self-explanatory, training-free mapping transformer based on non-deterministic finite automata, designed for Field-Programmable Gate Arrays (FPGAs) and addresses the challenges and considerations for enhancing the design in future iterations.

Abstract

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.

Figures (3)

• Figure 1: Example of Finite State Transducer
• Figure 2: Transformer Framework
• Figure 3: Preliminary Hardware Results