論文筆記-An Analysis of Deep Neural Network Models for Practical Applications
https://arxiv.org/abs/1605.07678 摘要 本文全面分析了在實際應用中的幾項重要指標：準確率、內存佔用、參數、操作數量、推理時間、功耗，並得出了以下幾項主要研究結論： （1）功耗與batch size和網絡架構無關； （2）準確率和推理時間呈雙曲線關係； （3）能量限制是最大可達準確率和模式複雜度的上限； （4）操作的數量可以有效的估計推理時間。 準確率： 圖1展示了提
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相關文章
- 1. Analysis of Drifting Dynamics with Neural Network Hidden Markov Models
- 2. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
- 3. Inspirational Applications of Deep Learning
- 4. 《NetAdapt：Platform-Aware Neural Network Adaptation for Mobile Applications》論文筆記
- 5. 1604.An Enhanced Deep Feature Representation for Person Re-identification 論文筆記
- 6. Applications of Graph Neural Networks
- 7. 論文筆記：Unsupervised Deep Embedding for Clustering Analysis
- 8. 論文筆記：DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification
- 9. 【論文筆記】Character-Aware Neural Language Models
- 10. DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration 2019 論文筆記