DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual
apps spanning from on-demand movies and 360° videos to video-conferencing and live …

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …

Convolutional neural networks (CNNs) have recently become the state-of-the-art in a
diversity of AI tasks. Despite their popularity, CNN inference still comes at a high …

The continued need for improvements in accuracy, throughput, and efficiency of Deep
Neural Networks has resulted in a multitude of methods that make the most of custom …

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

Abstract Convolutional Neural Network-based (CNN) inference is a demanding
computational task where a long sequence of operations is applied to an input as dictated …

During the recent years artificial neural networks have become a great part of everyday life.
One of the big problems in AI is acceleration of neural network inference using different …

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 …

The unprecedented performance of deep neural networks (DNNs) has led to large strides in
various Artificial Intelligence (AI) inference tasks, such as object and speech recognition …