Improving GPU performance in multimedia applications through FPGA based adaptive DMA controller

Abstract

Purpose Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation. Design/methodology/approach The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing. Findings This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization. Originality/value The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.