# A Tsetlin Machine Image Classification Accelerator on a Flexible Substrate

Source: [A Tsetlin Machine Image Classification Accelerator on a Flexible Substrate](<https://www.sota2.com/research/paper/68fa7c461e209131cea8d595>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T10:38:22.670Z

Authors: Yushu Qin, Marcos L. L. Sartori, Shengyu Duan, Emre Ozer, Rishad Shafik, Alex Yakovlev

Published: 2025-10-17T10:47:58.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.15519>)

[PDF](<https://arxiv.org/pdf/2510.15519.pdf>)