The Role of Advanced Computer Architectures in Accelerating Artificial Intelligence Workloads
Shahid Amin, Syed Pervez Hussnain Shah

TL;DR
This paper reviews how advanced computer architectures like GPUs, ASICs, and FPGAs have evolved to meet the computational demands of AI workloads, emphasizing design principles, emerging technologies, and the importance of hardware-software co-design.
Contribution
It provides a comprehensive survey of architectural paradigms for AI acceleration, analyzing design philosophies, performance trade-offs, and future directions such as PIM and neuromorphic computing.
Findings
GPU, ASIC, FPGA architectures are key to AI acceleration
Dataflow, memory hierarchies, sparsity, quantization improve performance
Emerging PIM and neuromorphic tech may redefine future AI hardware
Abstract
The remarkable progress in Artificial Intelligence (AI) is foundation-ally linked to a concurrent revolution in computer architecture. As AI models, particularly Deep Neural Networks (DNNs), have grown in complexity, their massive computational demands have pushed traditional architectures to their limits. This paper provides a structured review of this co-evolution, analyzing the architectural landscape designed to accelerate modern AI workloads. We explore the dominant architectural paradigms Graphics Processing Units (GPUs), Appli-cation-Specific Integrated Circuits (ASICs), and Field-Programmable Gate Ar-rays (FPGAs) by breaking down their design philosophies, key features, and per-formance trade-offs. The core principles essential for performance and energy efficiency, including dataflow optimization, advanced memory hierarchies, spar-sity, and quantization, are analyzed.…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Advanced Neural Network Applications · Big Data and Digital Economy
