# One-step regression and classification with crosspoint resistive memory arrays

arXiv: https://arxiv.org/abs/2005.01988 · HTML: https://mlsanity.com/paper/2005.01988/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

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

- [PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System](https://mlsanity.com/paper/2404.07164/) (2024)
- [An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2207.07886/) (2022)
- [Current Opinions on Memristor-Accelerated Machine Learning Hardware](https://mlsanity.com/paper/2501.12644/) (2025)
- [Harnessing intuitive local evolution rules for physical learning](https://mlsanity.com/paper/2507.19561/) (2025)
- [Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model](https://mlsanity.com/paper/2404.05648/) (2024)
- [Efficient and accurate neural field reconstruction using resistive memory](https://mlsanity.com/paper/2404.09613/) (2024)
- [Modern analog computing for solving differential and matrix equations](https://mlsanity.com/paper/2606.13179/) (2026)

## Abstract (arXiv)

Machine learning has been getting a large attention in the recent years, as a tool to process big data generated by ubiquitous sensors in our daily life. High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge, i.e., without the support of a remote frame server in the cloud. Such requirements challenge the complementary metal-oxide-semiconductor (CMOS) technology, which is limited by the Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures and devices are thus strongly needed to accelerate data-intensive applications. Here we show a crosspoint resistive memory circuit with feedback configuration can execute linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. The most elementary learning operation, that is the regression of a sequence of data and the classification of a set of data, can thus be executed in one single computational step by the novel technology. One-step learning is further supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. The results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.