A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms

TL;DR

This survey comprehensively reviews recent hardware accelerators for deep learning in high-performance computing, covering GPU, FPGA, ASIC, neuromorphic, quantum, and emerging memory technologies.

Contribution

It provides a detailed classification and analysis of the latest DL accelerators across diverse hardware platforms and emerging paradigms, highlighting recent advancements.

Findings

Diverse hardware accelerators have been developed for DL in HPC.

Emerging technologies like quantum and photonics are gaining interest.

Specialized accelerators outperform general-purpose hardware in specific tasks.

Abstract

Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent developments in DL accelerators, focusing on their role in meeting the performance demands of HPC applications. We explore cutting-edge approaches to DL acceleration, covering not only GPU- and TPU-based platforms but also specialized hardware such as FPGA- and ASIC-based accelerators, Neural Processing Units, open hardware RISC-V-based accelerators, and co-processors. This survey also describes accelerators leveraging emerging memory technologies and computing paradigms, including 3D-stacked Processor-In-Memory, non-volatile memories like Resistive RAM and Phase Change Memories used for in-memory computing, as well as Neuromorphic Processing Units, and Multi-Chip Module-based accelerators. Furthermore, we provide insights into emerging quantum-based accelerators and photonics. Finally, this survey categorizes the most influential architectures and technologies from recent years, offering readers a comprehensive perspective on the rapidly evolving field of deep learning acceleration.