Multi-DNN Accelerators for Next-Generation AI Systems
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 19 May 2022.
Builds on
6 of 19 references.
References · 6
- 2021Adaptive Inference through Early-Exit Networks: Design, Challenges and Directionscited by 74
- 2018NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Visioncited by 68
- 2020Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Taskscited by 43
- 2019PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Unitscited by 30
- 2018f-CNNx: A Toolflow for Mapping Multi-CNN Applications on FPGAscited by 6
- 2021unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generationcited by 4
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.