Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototy** to deployment. A decade later, in this article, we assess the progress …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Abstract Recent advances in Artificial Intelligence (AI) and Cyber-Physical Systems (CPS)
for aerospace applications have brought about new opportunities for the fast-growing …

In this article, we survey existing academic and commercial efforts to provide Field-
Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a …

The detection of out-of-distribution data points is a common task in particle physics. It is used
for monitoring complex particle detectors or for identifying rare and unexpected events that …

The market for remote sensing space-based applications is fundamentally limited by up-and
downlink bandwidth and onboard compute capability for space data handling systems. This …

Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

Although the quest for more accurate solutions is pushing deep learning research towards
larger and more complex algorithms, edge devices demand efficient inference and therefore …