A Tsetlin Machine Image Classification Accelerator on a Flexible Substrate

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.

Yushu Qin, Marcos L. L. Sartori, Shengyu Duan, Emre Ozer, Rishad Shafik, Alex Yakovlev · 2025