Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …

Deploying deep learning models on embedded systems for computer vision tasks has been
challenging due to limited compute resources and strict energy budgets. The majority of …

On the road to making self-driving cars a reality, academic and industrial researchers are
working hard to continue to increase safety while meeting technical and regulatory …

Deep neural network (DNN)-based object detection has been investigated and applied to
various real-time applications. However, it is hard to employ the DNNs in embedded …

Convolutional neural networks (CNNs) show high performance in computer vision tasks
including object detection, but a lot of weight storage and computation requirement prohibits …

Neural networks are the state-of-the-art solution to image-processing tasks. Some of these
neural networks are relatively simple, but the popular convolutional neural networks (CNNs) …

FPGAs, characterized by their low power consumption and swift response, are ideally suited
for parallel computations associated with object detection tasks, making them a popular …

Object detection and classification is a key task in many computer vision applications such
as smart surveillance and autonomous vehicles. Recent advances in deep learning have …

Convolutional neural networks (CNNs) have been widely deployed in deep learning
applications, especially on power hungry GP-GPUs. Recent efforts in designing CNN …

Convolutional neural network (CNN)-based object detection has achieved very high
accuracy; eg, single-shot multi-box detectors (SSDs) can efficiently detect and localize …