# [Learning automata based energy-efficient AI hardware design for IoT applications](https://www.literal-labs.ai/research/royal-society-ai-automata.html)

## Philosophical Transactions of the Royal Society

**Authors**: include Adrian Wheeldon, [Rishad Shafik](https://www.literal-labs.ai/about/rishad-shafik.html), Tousif Rahman, [Alex Yakovlev](https://www.literal-labs.ai/about/alex-yakovlev.html), [Ole-Christoffer Granmo](https://www.literal-labs.ai/about/ole-christoffer-granmo.html)  
**Published**: 2020, _The Royal Society_

### Abstract

The advancement of sensing devices has heralded the onset of the fourth industrial revolution, necessitating substantial progress in artificial intelligence (AI) for Internet of Things (IoT) applications. This technological shift demands AI systems that can process extensive data volumes in real-time, making energy efficiency a critical concern for hardware designers. Traditional neural network-based AI systems, with their complex arithmetic operations, struggle to meet these energy efficiency requirements, especially within the IoT context.

A significant knowledge gap exists in developing efficient AI hardware that maintains high learning accuracy while minimising energy consumption. This research addresses this gap by proposing an innovative AI hardware architecture founded on Tsetlin machines and Tsetlin automata. Tsetlin machines, a form of learning automata, use [propositional logic](https://www.literal-labs.ai/logical-ai/propositional-logic-ai.html) to reinforce actions based on past outcomes, thus simplifying the learning process. Tsetlin automata, the fundamental components of these machines, operate through discrete-step updates to optimise action decisions.

Our results showcase that the custom-designed integrated circuit, which utilises binarised input data processed through parallel logic blocks, achieves remarkable energy efficiency. The architecture demonstrates a 2.67x reduction in machine size without sacrificing inference accuracy. Extensive validation against contemporary machine learning algorithms indicates that this architecture not only matches but often surpasses them in both accuracy and robustness.

The findings suggest that Tsetlin machine-based AI hardware can revolutionise IoT applications by providing an energy-efficient and reliable alternative to conventional neural network-based systems. This advancement paves the way for the development of sustainable AI solutions capable of real-time decision-making, thereby propelling the continuous evolution and sophistication of IoT technologies.

### Read more

The full paper _Learning automata based energy-efficient AI hardware design for IoT applications_ is available from [The Royal Society](https://doi.org/10.1098/rsta.2019.0593).

### Article

Published by the Royal Society 16 October 2020, Volume 378, Issue 2182 of Philosophical Transactions Of The Royal Society A.

DOI: [rsta.2019.0593](https://doi.org/10.1098/rsta.2019.0593)  
PubMed: 32921236  
Published by: Royal Society  
Print ISSN: 1364-503X  
Online ISSN: 1471-2962

### Other research by our team

**[An Optimised Toolbox for Advanced Image Processing with Tsetlin Machine Composites](https://www.literal-labs.ai/tsetlin-machines/cifar-10-benchmarks.html)**

[](https://www.literal-labs.ai/tsetlin-machines/cifar-10-benchmarks.html)

**[MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications](https://www.literal-labs.ai/research/tsetlin-machine-system-on-chip.html)**

[](https://www.literal-labs.ai/research/tsetlin-machine-system-on-chip.html)

**[REDRESS: Generating Compressed Models for Edge Inference Using Tsetlin Machines](https://www.literal-labs.ai/research/compress-tsetlin-machine-automata.html)**

[](https://www.literal-labs.ai/research/compress-tsetlin-machine-automata.html)

**[Systematic Search for Optimal Hyper parameters of the Tsetlin Machine on MNIST Dataset](https://www.literal-labs.ai/research/optimise-hyperparameters-tsetlin-machine-mnist.html)**

[](https://www.literal-labs.ai/research/optimise-hyperparameters-tsetlin-machine-mnist.html)

**[Visualization of Machine Learning Dynamics in Tsetlin Machines](https://www.literal-labs.ai/research/tsetlin-machine-visualisation.html)**

[](https://www.literal-labs.ai/research/tsetlin-machine-visualisation.html)

**[Self-timed reinforcement learning using Tsetlin Machine](https://www.literal-labs.ai/research/tsetlin-machine-reinforcement-learning.html)**

[](https://www.literal-labs.ai/research/tsetlin-machine-reinforcement-learning.html)