# An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications

arXiv: https://arxiv.org/abs/1906.06603 · HTML: https://mlsanity.com/paper/1906.06603/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity](https://mlsanity.com/paper/2302.01302/) (2023)
- [CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators](https://mlsanity.com/paper/2401.12428/) (2024)
- [Brain-inspired Cognition in Next Generation Racetrack Memories](https://mlsanity.com/paper/2111.02246/) (2021)
- [Efficient Calibration for RRAM-based In-Memory Computing using DoRA](https://mlsanity.com/paper/2504.03763/) (2025)
- [Robust Reasoning and Learning with Brain-Inspired Representations under Hardware-Induced Nonlinearities](https://mlsanity.com/paper/2604.12079/) (2026)

## Abstract (arXiv)

The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data movements. The decade-old idea of leveraging in-memory processing to eliminate substantial data movements has returned and led extensive research activities. The effectiveness of in-memory processing heavily relies on memory scalability, which cannot be satisfied by traditional memory technologies. Emerging non-volatile memories (eNVMs) that pose appealing qualities such as excellent scaling and low energy consumption, on the other hand, have been heavily investigated and explored for realizing in-memory processing architecture. In this paper, we summarize the recent research progress in eNVM-based in-memory processing from various aspects, including the adopted memory technologies, locations of the in-memory processing in the system, supported arithmetics, as well as applied applications.