Boda-RTC: Productive generation of portable, efficient code for convolutional neural networks on mobile computing platforms
2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)(2016)
摘要
The popularity of neural networks (NNs) spans academia [1], industry [2], and popular culture [3]. In particular, convolutional neural networks (CNNs) have been applied to many image based machine learning tasks and have yielded strong results [4]. The availability of hardware/software systems for efficient training and deployment of large and/or deep CNN models is critical for the continued success of the field [5] [1]. Early systems for NN computation focused on leveraging existing dense linear algebra techniques and libraries [6] [7]. Current approaches use low-level machine specific programming [8] and/or closed-source, purpose-built vendor libraries [9]. In this work, we present an open source system that, compared to existing approaches, achieves competitive computational speed while achieving significantly greater portability. We achieve this by targeting the vendor-neutral OpenCL platform [10] using a code-generation approach. We argue that our approach allows for both: (1) the rapid development of new computational kernels for existing hardware targets, and (2) the rapid tuning of existing computational kernels for new hardware targets. Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform [11] for CNN deployment.
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关键词
computer vision,code generation,neural networks,mobile computing,convolution
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