Quantum Computing (QC) claims to improve the efficiency of solving complex problems,
compared to classical computing. When QC is applied to Machine Learning (ML) …

With the increasing complexity of digital systems, managing power dissipation and energy
consumption in digital circuits, particularly in emerging embedded systems for artificial …

Vector normalization is an important process in several algorithms. It is used in classical
physical calculations, mathematical techniques, and machine learning, which has witnessed …

Recent advancements in quantization and mixed-precision approaches offers substantial
opportunities to improve the speed and energy efficiency of Neural Networks (NN) …

The computing industry is forced to find alternative design approaches and computing
platforms to sustain increased power efficiency, while providing sufficient performance …

Multicore systems are utilized in a wide range of applications, from embedded systems to
high-performance applications. Controlling power consumption while maximizing …

The contents of this thesis describes the development of a benchmarking tool capable of
taking an approximate, combinatorial arithmetic circuit and analysing the power …

In the nano-scale era, the main objective of integrated circuits is to enhance speed while
minimizing power consumption and area. This demand has given rise to the concept of …

In recent years with the development of digital communications, applications for various
wireless networks necessitate a single-chip design to meet performance requirements. The …