In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …

The ever-increasing number of resource-constrained machine-type communication (MTC)
devices is leading to the critical challenge of fulfilling diverse communication requirements …

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 …

Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …

Convolutional neural networks (CNNs) have revolutionized the world of computer vision
over the last few years, pushing image classification beyond human accuracy. The …

Hybrid neural networks (hybrid-NNs) have been widely used and brought new challenges to
NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated …

Convolutional Neural Networks (CNNs) have revolutionized the world of image classification
over the last few years, pushing the computer vision close beyond human accuracy. The …

Deep learning (DL) has demonstrated great performance in various applications on
powerful computers and servers. Recently, with the advancement of more powerful mobile …

Digital accelerators in the latest generation of complementary metal–oxide–semiconductor
processes support, multiply, and accumulate (MAC) operations at energy efficiencies …

Deep learning has recently become immensely popular for image recognition, as well as for
other recognition and pattern matching tasks in, eg, speech processing, natural language …