In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks
that use binary values for activations and weights, instead of full precision values. With …

This paper presents a method for simplifying and quantizing a deep neural network (DNN)-
based object detector to embed it into a real-time edge device. For network simplification …

Binarized convolutional neural network (BCNN) is a promising and efficient technique
toward the landscape of Artificial Intelligence of Things (AIoT) applications. In-Memory …

The emergence of edge computing provides a new solution to big data processing in the
Internet of Things (IoT) environment. By combining edge computing with deep neural …

Video object detection and action recognition typically require deep neural networks (DNNs)
with huge number of parameters. It is thereby challenging to develop a DNN video …

Featuring with characteristics of convolutional neural network (CNN) and recurrent neural
network (RNN), hybrid neural network (H-NN) has been widely applied within the field of …

This paper presents a mixed-signal architecture for implementing Quantized Neural
Networks (QNNs) using flash transistors to achieve extremely high throughput with …

This paper presents Tulip, a new architecture for a binary neural network (BNN) that uses an
optimal schedule for executing the operations of an arbitrary BNN. It was constructed with …

To effectively minimize static power for a wide range of applications, power domains for
coarse-grained reconfigurable array (CGRA) architectures need to be more fine-grained …

Binary neural network (BNN) has shown great potential to be implemented with power
efficiency and high throughput. Compared with its counterpart, the convolutional neural …