中圖分類(lèi)號(hào)： TN919.3+1
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.200835
中文引用格式： 宋存洋，李欣. 自動(dòng)化構(gòu)建移動(dòng)端神經(jīng)網(wǎng)絡(luò)的技術(shù)研究[J].電子技術(shù)應(yīng)用，2020，46(12)：83-88.
英文引用格式： Song Cunyang，Li Xin. Research on the technology of automatic construction of mobile terminal neural networks[J]. Application of Electronic Technique，2020，46(12)：83-88.

Research on the technology of automatic construction of mobile terminal neural networks

Abstract： Deep learning has achieved great success in many areas such as computer vision, speech signal processing and natural language processing. However, as the neural architecture becomes more complex, the number of parameters increases rapidly. Designing efficient neural architectures requires expertise knowledge and quantities of repetitive experiments, which leads to high cost. Besides, the scale of neural networks running in mobile devices is strictly limited due to the power consumption limit and relatively small storage space. This paper proposes an efficient architecture searching algorithm for mobile devices. The main contributions include:(1)We propose an algorithm for calculating the number of floating point operations in neural networks under given architecture.(2)We improve an existing gradient based neural architecture search algorithm and propose a constraint NAS algorithm.(3)We search several efficient neural architectures by adding the constraint of the number of floating point operations to the architecture searching process and adjusting its strength. We train the searched neural networks, test their performance on image classification tasks and compare with other neural networks which are commonly used in industry. The experimental results show that the performance of the model searched by our method can reach the performance of mainstream models in industry.

Key words : lightweight neural network；convolution；model constraint；neural architecture search