# GraphChallenge.org Sparse Deep Neural Network Performance

arXiv: https://arxiv.org/abs/2004.01181 · HTML: https://mlsanity.com/paper/2004.01181/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Partitioning sparse deep neural networks for scalable training and inference](https://mlsanity.com/paper/2104.11805/) (2021)
- [Training for temporal sparsity in deep neural networks, application in video processing](https://mlsanity.com/paper/2107.07305/) (2021)
- [Activation Sparsity Opportunities for Compressing General Large Language Models](https://mlsanity.com/paper/2412.12178/) (2024)

## Abstract (arXiv)

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems. The sparse DNN challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment. In 2019 several sparse DNN challenge submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art sparse DNN execution time, Trm DNN, is a strong function of the number of DNN operations performed, Nrm op. The sparse DNN challenge provides a clear picture of current sparse DNN systems and underscores the need for new innovations to achieve high performance on very large sparse DNNs.