# Neuro-memristive Circuits for Edge Computing: A review

arXiv: https://arxiv.org/abs/1807.00962 · HTML: https://mlsanity.com/paper/1807.00962/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Signal Propagation: A Framework for Learning and Inference In a Forward Pass](https://mlsanity.com/paper/2204.01723/) (2022)
- [Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI](https://mlsanity.com/paper/2106.09166/) (2021)
- [Memristive Stochastic Computing for Deep Learning Parameter Optimization](https://mlsanity.com/paper/2103.06506/) (2021)
- [Optimizing for In-memory Deep Learning with Emerging Memory Technology](https://mlsanity.com/paper/2112.00324/) (2021)
- [Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edge](https://mlsanity.com/paper/2510.20997/) (2025)
- [AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars](https://mlsanity.com/paper/2111.07684/) (2021)
- [On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise](https://mlsanity.com/paper/2408.14680/) (2024)

## Abstract (arXiv)

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing.