Darknet on OpenCL: A multiplatform tool for object detection and classification

Abstract

The goal of this article is to overview the challenges and problems on the way from the state-of-the-art CUDA accelerated neural network code to multi-GPU code. For this purpose, the authors describe the journey of porting that existing in GitHub, a fully featured CUDA-accelerated Darknet engine, to OpenCL. This article presents the lessons learned and the techniques that were put in place for this porting. There are few other implementations on GitHub that leverage the OpenCL standard, and a few have tried to port Darknet as well. Darknet is a well-known convolutional neural network (CNN) framework. The authors of this article investigated all aspects of porting and achieved a fully featured Darknet engine on OpenCL. The effort was focused not only on classification using YOLO1, YOLO2, YOLO3, and YOLO4 CNN models.Other aspects were also covered, such as training neural networks and benchmarks to identify weak points in the implementation. Compared with the standard CPU version, the GPU computing code substantially improves the Darknet computing time by using underutilized hardware in existing systems. If the system is OpenCL-based, it is practically hardware-independent. The authors also improved the CUDA version as Darknet-vNext.

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“…It can be used with CPU and GPU. [11] The final detection is implemented using OpenCV as open source library. The programming language is Python (Version: 3.8.13, Delaware, USA).…”