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Implication of Optimizing NPU Dataflows on Neural Architecture Search for Mobile Devices

Implication of Optimizing NPU Dataflows on Neural Architecture Search for Mobile Devices
Lee, JooyeonPark, JunsangLee, SeunghyunKung, Jaeha
Issued Date
ACM Transactions on Design Automation of Electronic Systems, v.27, no.5
Author Keywords
Dataflow optimizationneural networksneural architecture searchneural processing unit
Recent advances in deep learning have made it possible to implement artificial intelligence in mobile devices. Many studies have put a lot of effort into developing lightweight deep learning models optimized for mobile devices. To overcome the performance limitations of manually designed deep learning models, an automated search algorithm, called neural architecture search (NAS), has been proposed. However, studies on the effect of hardware architecture of the mobile device on the performance of NAS have been less explored. In this article, we show the importance of optimizing a hardware architecture, namely, NPU dataflow, when searching for a more accurate yet fast deep learning model. To do so, we first implement an optimization framework, named FlowOptimizer, for generating a best possible NPU dataflow for a given deep learning operator. Then, we utilize this framework during the latency-aware NAS to find the model with the highest accuracy satisfying the latency constraint. As a result, we show that the searched model with FlowOptimizer outperforms the performance by 87.1% and 92.3% on average compared to the searched model with NVDLA and Eyeriss, respectively, with better accuracy on a proxy dataset. We also show that the searched model can be transferred to a larger model to classify a more complex image dataset, i.e., ImageNet, achieving 0.2%/5.4% higher Top-1/Top-5 accuracy compared to MobileNetV2-1.0 with 3.6x lower latency.
Association for Computing Machinary, Inc.
Related Researcher
  • 궁재하 Kung, Jaeha 전기전자컴퓨터공학과
  • Research Interests 딥러닝; 가속하드웨어; 저전력 하드웨어; 고성능 시스템
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Department of Electrical Engineering and Computer Science Intelligent Digital Systems Lab 1. Journal Articles


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