# Self-timed Reinforcement Learning using Tsetlin Machine

arXiv: https://arxiv.org/abs/2109.00846 · HTML: https://mlsanity.com/paper/2109.00846/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications](https://mlsanity.com/paper/1809.04547/) (2018)
- [Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier](https://mlsanity.com/paper/2011.08755/) (2020)
- [Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data](https://mlsanity.com/paper/2007.13885/) (2020)
- [A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning](https://mlsanity.com/paper/2007.02114/) (2020)
- [Low-Latency Asynchronous Logic Design for Inference at the Edge](https://mlsanity.com/paper/2012.03402/) (2020)

## Built on by (most cited first, 2 of 2)

- [An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator](https://mlsanity.com/paper/2501.19347/) (2025)
- [Efficient FPGA Implementation of Time-Domain Popcount for Low-Complexity Machine Learning](https://mlsanity.com/paper/2505.02181/) (2025)

## Abstract (arXiv)

We present a hardware design for the learning datapath of the Tsetlin machine algorithm, along with a latency analysis of the inference datapath. In order to generate a low energy hardware which is suitable for pervasive artificial intelligence applications, we use a mixture of asynchronous design techniques - including Petri nets, signal transition graphs, dual-rail and bundled-data. The work builds on previous design of the inference hardware, and includes an in-depth breakdown of the automaton feedback, probability generation and Tsetlin automata. Results illustrate the advantages of asynchronous design in applications such as personalized healthcare and battery-powered internet of things devices, where energy is limited and latency is an important figure of merit. Challenges of static timing analysis in asynchronous circuits are also addressed.