A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Citations Over TimeTop 1% of 2025 papers
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.
Related Papers
- → Overview of deep learning in medical imaging(2017)1,052 cited
- → A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis(2020)4 cited
- → Deep fake Detection Through Deep Learning(2023)4 cited
- Universal Command Guide: For Operating Systems(2002)
- Why & When Deep Learning Works: Looking Inside Deep Learnings.(2017)