# Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network

arXiv: https://arxiv.org/abs/2105.02953 · HTML: https://mlsanity.com/paper/2105.02953/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 20 Apr 2021.

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

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers](https://mlsanity.com/paper/1905.12107/) (2019)
- [Any-Precision Deep Neural Networks](https://mlsanity.com/paper/1911.07346/) (2019)
- [FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network](https://mlsanity.com/paper/1901.02358/) (2019)
- [Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization](https://mlsanity.com/paper/1803.09327/) (2018)
- [Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map](https://mlsanity.com/paper/2006.02824/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks](https://mlsanity.com/paper/2107.08399/) (2021)
- [An improved LogNNet classifier for IoT application](https://mlsanity.com/paper/2105.14412/) (2021)
- [A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare](https://mlsanity.com/paper/2108.02428/) (2021)
- [A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science](https://mlsanity.com/paper/2306.01991/) (2023)
- [Deep Microcompression: Structured Pruning and Bit-packed Quantization for Microcontrollers](https://mlsanity.com/paper/2609.05081/) (2026)
- [Smart Application for Fall Detection Using Wearable ECG & Accelerometer Sensors](https://mlsanity.com/paper/2207.00008/) (2022)

## Abstract (arXiv)

The presented compact algorithm for recognizing handwritten digits of the MNIST database, created on the LogNNet reservoir neural network, reaches the recognition accuracy of 82%. The algorithm was tested on a low-memory Arduino board with 2 Kb static RAM low-power microcontroller. The dependences of the accuracy and time of image recognition on the number of neurons in the reservoir have been investigated. The memory allocation demonstrates that the algorithm stores all the necessary information in RAM without using additional data storage, and operates with original images without preliminary processing. The simple structure of the algorithm, with appropriate training, can be adapted for wide practical application, for example, for creating mobile biosensors for early diagnosis of adverse events in medicine. The study results are important for the implementation of artificial intelligence on peripheral constrained IoT devices and for edge computing.