FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Machine Embeddings

Embedding models in natural language processing (NLP) have achieved remarkable success, but their representations often lack interpretability. The Tsetlin Machine (TM) provides an interpretable, logic-based alternative. Omni TM Autoencoder (Omni TM-AE) applies this paradigm to static embeddings, but its training remains slow. In this work, we propose FastOmniTMAE, which replaces sequential training dependencies with a two-stage parallel process of evaluation and update. Across classification, similarity, and clustering tasks, FastOmniTMAE achieves up to 5$\times$ faster training while maintaining comparable embedding quality. We further implement FastOmniTMAE as a reusable accelerator on SoC-FPGA platforms, achieving similarity scores of 0.669 on a resource-constrained FPGA and 0.696 on an UltraScale+ SoC. These results demonstrate an efficient and interpretable approach to logic-based embedding training with a small hardware footprint.