This paper introduces the first implementation of digital Tsetlin Machines (TMs) on flexible integrated circuit (FlexIC) using Pragmatic's 600nm IGZO-based FlexIC technology. TMs, known for their energy efficiency, interpretability, and suitability for edge computing, have previously been limited by the rigidity of conventional silicon-based chips. We develop two TM inference models as FlexICs: one achieving 98.5% accuracy using 6800 NAND2 equivalent logic gates with an area of 8X8 mm2, and a second more compact version achieving slightly lower prediction accuracy of 93% but using only 1420 NAND2 equivalent gates with an area of 4X4 mm2, both of which are custom-designed for an 8X8-pixel handwritten digit recognition dataset. The paper demonstrates the feasibility of deploying flexible TM inference engines into wearable healthcare and edge computing applications.
@article{arxiv.2510.15519,
title = {A Tsetlin Machine Image Classification Accelerator on a Flexible Substrate},
author = {Yushu Qin and Marcos L. L. Sartori and Shengyu Duan and Emre Ozer and Rishad Shafik and Alex Yakovlev},
journal= {arXiv preprint arXiv:2510.15519},
year = {2025}
}
Comments
accepted by International Symposium on the Tsetlin Machine (ISTM) 2025