# Sparse Deep Neural Network Graph Challenge

arXiv: https://arxiv.org/abs/1909.05631 · HTML: https://mlsanity.com/paper/1909.05631/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

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

- [Truly Sparse Neural Networks at Scale](https://mlsanity.com/paper/2102.01732/) (2021)
- [Sparse Training Theory for Scalable and Efficient Agents](https://mlsanity.com/paper/2103.01636/) (2021)
- [Partitioning sparse deep neural networks for scalable training and inference](https://mlsanity.com/paper/2104.11805/) (2021)
- [FSD-Inference: Fully Serverless Distributed Inference with Scalable Cloud Communication](https://mlsanity.com/paper/2403.15195/) (2024)
- [Maneuver Identification Challenge](https://mlsanity.com/paper/2108.11503/) (2021)
- [AI Enabled Maneuver Identification via the Maneuver Identification Challenge](https://mlsanity.com/paper/2211.15552/) (2022)
- [SparseCraft: Agentic Hardware-Software Co-Optimization for Sparse Computing](https://mlsanity.com/paper/2610.05037/) (2026)

## 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 proposed 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. Sparse DNN inference is amenable to both vertex-centric implementations and array-based implementations (e.g., using the GraphBLAS.org standard). The computations are simple enough that performance predictions can be made based on simple computing hardware models. The input data sets are derived from the MNIST handwritten letters. The surrounding I/O and verification provide the context for each sparse DNN inference that allows rigorous definition of both the input and the output. Furthermore, since the proposed sparse DNN challenge is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Reference implementations have been implemented and their serial and parallel performance have been measured. Specifications, data, and software are publicly available at GraphChallenge.org