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 …

Due to less memory requirement, low computation overhead and negligible accuracy
degradation, deep neural networks with binary/ternary weights (BTNNs) have been widely …

The growing interest in using FPGAs to accelerate convolutional neural network (CNN)
workloads is driving the deployment of FPGAs on cloud services such as Amazon AWS and …

Human Activity Recognition requires very high accuracy to be effectively employed into
practical applications, ranging from elderly care to microsurgical devices. The highest …

A custom Human Activity Recognition system is presented based on the resource-
constrained Hardware (HW) implementation of a new partially binarized Hybrid Neural …

Single computation engines have become a popular design choice for FPGA-based
convolutional neural networks (CNNs) enabling the deployment of diverse models without …

The unprecedented accuracy of convolutional neural networks (CNNs) across a broad
range of AI tasks has led to their widespread deployment in mobile and embedded settings …

Designing hardware accelerators to run the inference of convolutional neural networks
(CNN) is under intensive research. Several different architectures have been proposed …

In recent years, Convolutional Neural Networks (CNNs) have become the standard class of
deep neural network for image processing, classification and segmentation tasks. However …

Yield is key to profitability in semiconductor manufacturing and controlling the fabrication
process is therefore a key duty for engineers in silicon foundries. Analyzing the distribution …