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Resource-Constrained Edge AI with early exit prediction

Journal of Communications and Information Networks 2022年第2期7卷 122-134页

作者： Rongkang Dong Yuyi Mao Jun Zhang Department of Electronic and Information Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

By leveraging the data sample diversity,the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process.However,intermediate classifiers of the early exits introduce additional computation overhead,which is unfavorable for resource-constrained edge artificial intelligence(AI).In this paper,we propose an early exit prediction mechanism to reduce the on-device computation overhead in a device-edge co-inference system supported by early-exit networks.Specifically,we design a low-complexity module,namely the exit predictor,to guide some distinctly“hard”samples to bypass the computation of the early exits.Besides,considering the varying communication bandwidth,we extend the early exit prediction mechanism for latency-aware edge inference,which adapts the prediction thresholds of the exit predictor and the confidence thresholds of the early-exit network via a few simple regression models.Extensive experiment results demonstrate the effectiveness of the exit predictor in achieving a better tradeoff between accuracy and on-device computation overhead for early-exit networks.Besides,compared with the baseline methods,the proposed method for latency-aware edge inference attains higher inference accuracy under different bandwidth conditions.

关键词： artificial intelligence(AI) edge AI device-edge cooperative inference early-exit network early exit prediction

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