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Creators/Authors contains: "Koga, Brian"

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  1. ; ; ; (, IEEE)
  2. ; ; ; (, 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