# Memristive Computing for Efficient Inference on Resource Constrained Devices

arXiv: https://arxiv.org/abs/2208.10490 · HTML: https://mlsanity.com/paper/2208.10490/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 21 Aug 2022.

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

- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM](https://mlsanity.com/paper/1908.11691/) (2019)
- [N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections](https://mlsanity.com/paper/2208.03662/) (2022)
- [Spectral Reflectance based Heart Rate Measurement from Facial Video](https://mlsanity.com/paper/1901.00273/) (2019)
- [A neuromorphic approach to image processing and machine vision](https://mlsanity.com/paper/2209.02595/) (2022)
- [Implementation of fast ICA using memristor crossbar arrays for blind image source separations](https://mlsanity.com/paper/2208.04317/) (2022)

## Abstract (arXiv)

The advent of deep learning has resulted in a number of applications which have transformed the landscape of the research area in which it has been applied. However, with an increase in popularity, the complexity of classical deep neural networks has increased over the years. As a result, this has leads to considerable problems during deployment on devices with space and time constraints. In this work, we perform a review of the present advancements in non-volatile memory and how the use of resistive RAM memory, particularly memristors, can help to progress the state of research in deep learning. In other words, we wish to present an ideology that advances in the field of memristive technology can greatly influence and impact deep learning inference on edge devices.