Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

TL;DR

This paper introduces Boda-RTC, an open source system that generates portable and efficient code for CNNs on mobile platforms using OpenCL, enabling rapid development and tuning across hardware.

Contribution

It presents a vendor-neutral code-generation approach for CNN deployment on mobile devices, improving portability and development speed.

Findings

Achieves competitive computational speed on mobile hardware

Higher portability compared to vendor-specific libraries

Enables rapid kernel development and tuning

Abstract

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.