Accelerating Native Inference Model Performance in Edge Devices using TensorRT

In this era, deep learning is becoming increasingly popular for solving real-world problems. Due to the extremely high processing power required to execute the most complex deep learning models, Graphics Processing Units(GPUs) and Neural Processing Units(NPUs) are essential for optimal execution. However, it is challenging to incorporate these high-performance processors in every edge device like Internet of Things gadgets, embedded systems, and cell phones to execute complex inference models. Hence, deep learning models must be optimized to perform well on edge devices with limited memory, power, and computing capabilities. Optimizing the inference models to accelerate performance on edge devices is one of the significant issues facing, especially in complex computation domains like computer vision. TensorRT, introduced by NVIDIA, is one of the best approaches used to optimize inference models, by accelerating their performance on edge devices without compromising the model accuracy. It presents a variety of optimization approaches, including layer fusion, quantization, dynamic tensor memory management, kernel auto tuning, and precision calibration. Every optimization technique is contingent upon the problem that have selected and the use of all optimization approaches has the potential to significantly speed up the performance of an inference model. This paper explores how TensorRT accelerates the performance of inference models like ResNet-50, VGG16, and Inception V3 in an image classification problem, and the results are analyzed with its native inference models. The utilization of TensorRT in edge devices is expanding significantly, reflecting a growing scope for enhanced performance and efficiency in processing complex neural networks at the edge.