Neuro-memristive Circuits for Edge Computing: A review
Not on the map. Why?
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
7 papers.
- 2022Signal Propagation: A Framework for Learning and Inference In a Forward Passcited by 24
- 2021Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AIcited by 10
- 2021Memristive Stochastic Computing for Deep Learning Parameter Optimizationcited by 3
- 2021Optimizing for In-memory Deep Learning with Emerging Memory Technologycited by 1
- 2025Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edgecited by 1
- 2021AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbarscited by 0
- 2024On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noisecited by 0
- 2025Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edge23 Oct 2025
- 2024On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise26 Aug 2024
- 2022Signal Propagation: A Framework for Learning and Inference In a Forward Pass4 Apr 2022
- 2021Optimizing for In-memory Deep Learning with Emerging Memory Technology1 Dec 2021
- 2021AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars15 Nov 2021
- 2021Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI16 Jun 2021
- 2021Memristive Stochastic Computing for Deep Learning Parameter Optimization11 Mar 2021