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Award ID contains: 1906541

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  1. ; ; ; ; ( , The International Conference for High Performance Computing, Networking, Storage, and Analysis)
  2. ; ; ; ; ; ; ; ( , the 29th IEEE International Symposium on High-Performance Computer Architecture (HPCA-29))
  3. ; ; ; ( , IEEE)
  4. ; ; ; ; ; ; ( , IEEE Transactions on Parallel and Distributed Systems)
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  5. ; ; ; ; ( , IEEE)
  6. ; ( , The 34th International Conference on Scientific and Statistical Database Management)
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  7. ; ; ; ; ; ; ( , The 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)
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  8. ; ( , 2021 IEEE International Conference on Networking, Architecture and Storage (NAS))
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  9. ; ; ; ( , 3rd Workshop on Accelerated Machine Learning (AccML) Co-located with the HiPEAC 2021 Conference)
    null (Ed.)
    Deep neural networks (DNNs) have recently gained unprecedented success in various domains. In resource-constrained systems, QoS-aware DNNs are designed to meet latency requirements of mission-critical deep learning applications. However, none of the existing DNNs have been designed to satisfy both latency and memory bounds simultaneously as specified by end-users in the resource-constrained systems. In this paper, we propose BLINKNET, a runtime system that is able to guarantee both latency and memory/storage bounds via efficient QoS-aware per-layer approximation. We implement BLINKNET in Apache TVM and evaluate it using Cifar10-quick and VGG network models. Our experimental results show that BLINKNET can meet the latency and memory requirements with 2% accuracy loss on average. 
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    Accepted Manuscript Full Text Available
  10. ; ; ; ( , Proceedings of the 34th ACM International Conference on Supercomputing)
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