# Multi-DNN Accelerators for Next-Generation AI Systems

arXiv: https://arxiv.org/abs/2205.09376 · HTML: https://mlsanity.com/paper/2205.09376/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 6 of 6)

- [Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions](https://mlsanity.com/paper/2106.05022/) (2021)
- [NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision](https://mlsanity.com/paper/1810.10090/) (2018)
- [Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks](https://mlsanity.com/paper/2002.04116/) (2020)
- [PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units](https://mlsanity.com/paper/1909.04548/) (2019)
- [f-CNNx: A Toolflow for Mapping Multi-CNN Applications on FPGAs](https://mlsanity.com/paper/1805.10174/) (2018)
- [unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation](https://mlsanity.com/paper/2103.05600/) (2021)

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

- [Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices](https://mlsanity.com/paper/2408.08015/) (2024)
- [The Future of Consumer Edge-AI Computing](https://mlsanity.com/paper/2210.10514/) (2022)
- [Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads](https://mlsanity.com/paper/2310.11096/) (2023)

## Abstract (arXiv)

As the use of AI-powered applications widens across multiple domains, so do increase the computational demands. Primary driver of AI technology are the deep neural networks (DNNs). When focusing either on cloud-based systems that serve multiple AI queries from different users each with their own DNN model, or on mobile robots and smartphones employing pipelines of various models or parallel DNNs for the concurrent processing of multi-modal data, the next generation of AI systems will have multi-DNN workloads at their core. Large-scale deployment of AI services and integration across mobile and embedded systems require additional breakthroughs in the computer architecture front, with processors that can maintain high performance as the number of DNNs increases while meeting the quality-of-service requirements, giving rise to the topic of multi-DNN accelerator design.