# Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices

arXiv: https://arxiv.org/abs/1912.09083 · HTML: https://mlsanity.com/paper/1912.09083/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

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

- [Reliable Fleet Analytics for Edge IoT Solutions](https://mlsanity.com/paper/2101.04414/) (2021)
- [Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPT](https://mlsanity.com/paper/2405.01419/) (2024)

## Abstract (arXiv)

This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising need of processing data generated by natural person (a human being), also known as personal data. Spiking Neural networks are the core method behind it: suitable for a low latency energy-constrained hardware, enabling local training or re-training, while not taking advantage of scalability available in the Cloud.