The use of Artificial Intelligence (ai), and more specifically of Deep Learning (dl), in modern
software systems, is nowadays widespread and continues to grow. At the same time, its …

Sparse deep neural network (DNN) has become an important technique for reducing the
inference cost of large DNNs. However, computing large sparse DNNs is very challenging …

Real-time semantic segmentation of remote sensing imagery is a challenging task that
requires a tradeoff between effectiveness and efficiency. It has many applications, including …

This work is an introduction and a survey for the Special Issue on Machine Intelligence at the
Edge. The authors argue that workloads that were formerly performed in the cloud are …

Remarkable progress has been reported in the deployment of artificial intelligence and
machine learning applications in a broad range of capabilities such as healthcare, game …

Edge computing devices inherently face tight resource constraints, which is especially
apparent when deploying Deep Neural Networks (DNN) with high memory and compute …

Abstract Accurate and efficient Machine Learning algorithms are of vital importance to many
problems, especially on classification or clustering tasks but need a universal AI model …

Abstract Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have
been used for accelerating computations in several domains because of their unique …

In this paper we propose SECDA-TFLite, a new open source toolkit for developing DNN
hardware accelerators integrated within the TFLite framework. The toolkit leverages the …

This study presents advanced neural network architectures including Convolutional Neural
Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks …