Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms
This work addresses the need for portable and efficient deployment of CNNs on mobile hardware, which is an incremental improvement over existing vendor-specific or low-level methods.
The authors tackled the problem of generating efficient and portable code for convolutional neural networks on mobile platforms by developing Boda-RTC, an open-source system that uses OpenCL and code generation to achieve competitive computational speed while improving portability compared to existing approaches.
The popularity of neural networks (NNs) spans academia, industry, and popular culture. In particular, convolutional neural networks (CNNs) have been applied to many image based machine learning tasks and have yielded strong results. The availability of hardware/software systems for efficient training and deployment of large and/or deep CNN models has been, and continues to be, an important consideration for the field. Early systems for NN computation focused on leveraging existing dense linear algebra techniques and libraries. Current approaches use low-level machine specific programming and/or closed-source, purpose-built vendor libraries. In this work, we present an open source system that, compared to existing approaches, achieves competitive computational speed while achieving higher portability. We achieve this by targeting the vendor-neutral OpenCL platform 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 for CNN deployment.