MAPLE-Edge: A Runtime Latency Predictor for Edge Devices-Reference-Cited by-同舟云学术

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MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Author:

Nair Saeejith¹,Abbasi Saad¹,Wong Alexander¹,Shafiee Mohammad Javad¹

Affiliation:

1. University of Waterloo,Waterloo,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9856930/9856648/09857090.pdf?arnumber=9857090

Reference26 articles.

1. Hardware-adaptive efficient latency prediction for nas via meta-learning;lee;Advances in neural information processing systems,2021

2. Efficient neural architecture search via parameters sharing;pham;International Conference on Machine Learning,2018

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1. Fine-grained complexity-driven latency predictor in hardware-aware neural architecture search using composite loss;Information Sciences;2024-08

2. SLAPP: Subgraph-level attention-based performance prediction for deep learning models;Neural Networks;2024-02

3. Conformal Prediction Based Confidence for Latency Estimation of DNN Accelerators: A Black-Box Approach;IEEE Access;2024

4. Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture;2023 IEEE International Conference on Robotics and Biomimetics (ROBIO);2023-12-04

5. Accurate Latency Prediction of Deep Learning Model Inference Under Dynamic Runtime Resource;Communications in Computer and Information Science;2023-11-13

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