Edge devices are seeing tremendous growth in sensing and computational capabilities.
Running state-of-the-art deep neural network (NN) based data processing on multi-core …

The recent explosion in the popularity of Deep Learning (DL) is due to a combination of
improved algorithms, access to large datasets and increased computational power. This had …

Deep learning researchers and practitioners usually leverage GPUs to help train their deep
neural networks (DNNs) faster. However, choosing which GPU to use is challenging both …

The recent surge of Deep Learning (DL) models and applications can be attributed to the
rise in computational resources, availability of large-scale datasets, and accessible DL …

EdgeAI (Edge computing based Artificial Intelligence) has been most actively researched for
the last few years to handle variety of massively distributed AI applications to meet up the …

Training deep learning models is compute-intensive and there is an industry-wide trend
towards hardware specialization to improve performance. To systematically benchmark …

Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous
vehicles and smart cities through low-latency inferencing on edge computing devices close …

The ever increasing size of deep neural network (DNN) models once implied that they were
only limited to cloud data centers for runtime inference. Nonetheless, the recent plethora of …

Traditionally, Deep Learning (DL) frameworks like Caffe, TensorFlow, and Cognitive Toolkit
exploited GPUs to accelerate the training process. This has been primarily achieved by …

With the fast development of deep neural networks (DNNs), many real-world applications
are adopting multiple models to conduct compound tasks, such as co-running classification …