Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

The number of devices connected to the Internet is increasing, exchanging large amounts of
data, and turning the Internet into the 21st-century silk road for data. This road has taken …

In this work, we study the 1-bit convolutional neural networks (CNNs), of which both the
weights and activations are binary. While being efficient, the classification accuracy of the …

Keyword spotting (KWS) is a critical component for enabling speech based user interactions
on smart devices. It requires real-time response and high accuracy for good user …

Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices
performing data analytics right at the source, reducing latency as well as energy …

An energy-efficient deep neural network (DNN) accelerator, unified neural processing unit
(UNPU), is proposed for mobile deep learning applications. The UNPU can support both …

Neural network quantization has become an important research area due to its great impact
on deployment of large models on resource constrained devices. In order to train networks …

In the field of object detection, deep learning has greatly improved accuracy compared to
previous algorithms and has been used widely in recent years. However, object detection …

With the ever increasing application of Convolutional Neural Networks to customer products
the need emerges for models to efficiently run on embedded, mobile hardware. Slimmer …

This article analyzes the effects of approximate multiplication when performing inferences on
deep convolutional neural networks (CNNs). The approximate multiplication can reduce the …