Power consumption constitutes a major challenge for electronics circuits. One possible way
to deal with this issue is to consider it very soon in the design process in order to explore …

This work presents a dynamically reconfigurable architecture for Neural Network (NN)
accelerators implemented in Field-Programmable Gate Array (FPGA) that can be applied in …

Generating Gaussian random numbers is essential in many applications such as
cryptography, games, and computer simulations. Although software Gaussian Random …

Deep Neural Networks (DNNs) have demonstrated their utility across a wide range of input
data types, usable across diverse computing substrates, from edge devices to datacenters …

True random number generation on resource-constrained devices is challenging due to
inherent hardware limitations; these limitations affect the ability to find a reliable source of …

SRAM-Based FPGAs represent a low-cost alternative to ASIC device thanks to their high
performance and design flexibility. In particular, for aerospace and avionics application …

Digital signal processing besides multimedia applications needs plenty of data, real-time
processing capacity, and high computational power. Thus, adaptable architectures with run …

Partial reconfiguration supports virtualisation of applications on FPGAs, enabling compute to
dynamically adapt to workloads in distributed infrastructure and datecenters. While the latter …

Existing in-storage processing (ISP) techniques mainly focus on maximizing data processing
rate by always utilizing total storage data processing resources for all applications. We find …

Growth in edge computing has increased the requirement for edge systems to process
larger volumes of real-time data, such as with image processing and machine learning; …