# Dataflow Accelerator Architecture for Autonomous Machine Computing

arXiv: https://arxiv.org/abs/2109.07047 · HTML: https://mlsanity.com/paper/2109.07047/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines](https://mlsanity.com/paper/2012.01353/) (2020)
- [Rise of the Autonomous Machines](https://mlsanity.com/paper/2106.13987/) (2021)
- [PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design](https://mlsanity.com/paper/1710.00893/) (2017)

## Abstract (arXiv)

Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software.