Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

Given recent algorithm, software, and hardware innovation, computing has enabled a
plethora of new applications. As computing becomes increasingly ubiquitous, however, so …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …

A recent trend in deep neural network (DNN) development is to extend the reach of deep
learning applications to platforms that are more resource and energy-constrained, eg …

Abstract In the Internet of Things enabled intelligent transportation systems, a huge amount
of vehicle video data has been generated and real-time and accurate video analysis are …