Adaptive Balance of Quality-resources of Neural Networks with Principle Component Analysis
文献类型：会议论文
| 作者 | Cuiping Shao; Huiyun Li; Zhiheng Yang; Jiayan Fang |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Jeju, Korea |
| 英文摘要 | Deep Neural Networks (DNNs) push the state-of-the-art in many machine learning applications, they often require millions of expensive floating-point operations for each input classification. This computation overhead limits the applicability of DNNs to low-power, embedded platforms and incurs high cost in data centers. In this paper, by exploiting the gap between the level of accuracy required by the applications/users and that provided by the computing system, we propose a model to balance the resource and quality of neural network with principal components analysis (PCA), which simplifies network structure (such as the number of input nodes and the number of network layers) by reducing the dimension of dataset and achieve diverse optimizations. The continuous iteration and optimization of the balance model can acquire the best balance of quality and resources of the neural network. As a result, the consumption of network resources is reduced to the greatest extent under the condition of satisfying the quality requirements. In this paper, a pedestrian recognition network is taken as an example. The results illustrate that the total number of network nodes is reduced by 69% while the recognition rate achieved (95.99%) can reach required level of recognition rate (95%), which is only 2% lower than the highest recognition rate (97.01%) of the original neural network of pedestrian recognition. |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13740]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Cuiping Shao,Huiyun Li,Zhiheng Yang,et al. Adaptive Balance of Quality-resources of Neural Networks with Principle Component Analysis[C]. 见:. Jeju, Korea. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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