# Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators

arXiv: https://arxiv.org/abs/1912.12167 · HTML: https://mlsanity.com/paper/1912.12167/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [On the Accuracy of Analog Neural Network Inference Accelerators](https://mlsanity.com/paper/2109.01262/) (2021)
- [Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat](https://mlsanity.com/paper/2404.15781/) (2024)
- [Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency](https://mlsanity.com/paper/2509.17695/) (2025)
- [A Construction Kit for Efficient Low Power Neural Network Accelerator Designs](https://mlsanity.com/paper/2106.12810/) (2021)
- [Forward Target Propagation: A Forward-Only Approach to Global Error Credit Assignment via Local Losses](https://mlsanity.com/paper/2506.11030/) (2025)

## Abstract (arXiv)

This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators. We highlight important properties of these accelerators and the resulting design considerations using experiments conducted on various state-of-the-art deep neural networks with the large-scale ImageNet dataset.