Exploring the Performance of Deep Neural Networks on Embedded Many-Core Processors

Author:

Yabe Takuma1,Azumi Takuya1

Affiliation:

1. Saitama University,Saitama,Japan

Publisher

IEEE

Reference23 articles.

1. Mobilenets: Efficient convolutional neural networks for mobile vision applications;howard;ArXiv Preprint,2017

2. Speed/Accuracy Trade-Offs for Modern Con-volutional Object Detectors;huang;Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR),0

3. Autoware on Board: Enabling Autonomous Vehi-cles with Embedded Systems;kato;Proceedings of IEEE conference on International Conference on Cyber-Physical Systems (ICCPS),0

4. Deep embedded hybrid CNN - LSTM network for lane detection on NVIDIA Jetson Xavier NX;yassin;Knowledge-Based Systems,2022

5. Focal Loss for Dense Object Detection

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2. Neural inference at the frontier of energy, space, and time;Science;2023-10-20

3. DAG Scheduling for Clustered Many-Core Processor Considering Execution-Time-Reduction Effectiveness;2023 IEEE/ACM 27th International Symposium on Distributed Simulation and Real Time Applications (DS-RT);2023-10-04

4. DAG Scheduling Considering Parallel Execution for High-Load Processing on Clustered Many-core Processors;2022 IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications (DS-RT);2022-09-26

5. LET Paradigm Scheduling Algorithm Considering Parallel Processing on Clustered Many-core Processor;Journal of Information Processing;2022

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