# Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware

arXiv: https://arxiv.org/abs/1706.00280 · HTML: https://mlsanity.com/paper/1706.00280/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2017.

## Built on by (most cited first, 11 of 11)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Computing on Functions Using Randomized Vector Representations](https://mlsanity.com/paper/2109.03429/) (2021)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing](https://mlsanity.com/paper/2106.09821/) (2021)
- [Hyperseed: Unsupervised Learning with Vector Symbolic Architectures](https://mlsanity.com/paper/2110.08343/) (2021)
- [Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks](https://mlsanity.com/paper/2106.00881/) (2021)
- [Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System](https://mlsanity.com/paper/2405.13347/) (2024)
- [On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks](https://mlsanity.com/paper/2106.09831/) (2021)
- [A novel Reservoir Architecture for Periodic Time Series Prediction](https://mlsanity.com/paper/2405.10102/) (2024)
- [Reservoir Network with Structural Plasticity for Human Activity Recognition](https://mlsanity.com/paper/2503.00393/) (2025)
- [Direct Spatial Implementation of Sparse Matrix Multipliers for Reservoir Computing](https://mlsanity.com/paper/2101.08884/) (2021)

## Abstract (arXiv)

We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The proposed intESN approach is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss. The experiments on a field-programmable gate array confirm that the proposed intESN approach is much more energy efficient than the conventional ESN.