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GTC On-Demand

AI Application Deployment and Inference
Presentation
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Accelerating Large-Scale Video Surveillance for Smart Cities with TensorRT
Shounan An (SK Telecom)
We'll discuss a detailed scale-up method for accelerating deep learning-based object detection inference engine with INT8 by using NVIDIA's TensorRT. Previously, converting convolutional neural networks (CNNs) from 32-bit floating-point arithmetic ...Read More
We'll discuss a detailed scale-up method for accelerating deep learning-based object detection inference engine with INT8 by using NVIDIA's TensorRT. Previously, converting convolutional neural networks (CNNs) from 32-bit floating-point arithmetic (FP32) to 8-bit integer (INT8) for classification tasks has been researched. However, there is no solid work for accelerating CNN-based object detection tasks. We'll explain how to accelerate YOLO-v2, the state-of-the-art CNN-based object detector with TensorRT using INT8. We improved YOLO-v2 network for better acceleration and more accurate for surveillance and named our network SIDNet. We verified SIDNet on several benchmark object detection and intrusion detection datasets and confirmed that SIDNet with INT8 has only 1% accuracy drop compared with FP32 mode and is 5x faster than the original YOLO-v2 on NVIDIA Tesla P40.  Back
 
Keywords:
AI Application Deployment and Inference, Telecom Industry Solutions, Deep Learning and AI Frameworks, Computer Vision, Robotics & Autonomous Machines, GTC 2018 - ID S8296
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