Recent advancements in Artificial Intelligence (AI) algorithms have sparked a race to
enhance hardware capabilities for accelerated task processing. While significant strides …

The increasing computational and memory requirements of Deep Learning (DL) workloads
has led to outstanding innovations in hardware architectures. An archetype of such …

Artificial Intelligence (AI) neural networks have proven to be powerful tools for solving
complex problems. However, their real-world applications face challenges, particularly in …

Artificial Intelligence (AI) has revolutionized the tech industry, transforming the way we
interact with our surroundings, work, and live. However, AI's data and math-intensive …

The remarkable progress in Artificial Intelligence (AI) has been made possible by a “perfect
storm” emerging over the past two decades, bringing together tremendous progress in …

The convergence of High-Performance Computing (HPC) and Artificial Intelligence (AI) has
become a promising trend. Due to the different computation patterns of HPC and AI …

The recent advances in Artificial Intelligence (AI) achieving “better-than-human” accuracy in
a variety of tasks such as image classification and the game of Go have come at the cost of …

As benefits from Moore's Law diminish [1], general-purpose compute platforms like CPUs
and GPUs continue to become increasingly power inefficient. The majority of workloads for …

Artificial intelligence (AI) algorithms are extremely computational-intensive on voluminous
data. AI accelerators are desired to satisfy their hardware demands. This chapter introduces …

As the increasing complexity of Neural Network (NN) models leads to high demands for
computation, AMD introduces a heterogeneous programmable system-on-chip (SoC), ie …