A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
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2023
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| author | Silvano, Cristina Ielmini, Daniele Ferrandi, Fabrizio Fiorin, Leandro Curzel, Serena Benini, Luca Conti, Francesco Garofalo, Angelo Zambelli, Cristian Calore, Enrico Schifano, Sebastiano Fabio Palesi, Maurizio Ascia, Giuseppe Patti, Davide Petra, Nicola De Caro, Davide Lavagno, Luciano Urso, Teodoro Cardellini, Valeria Cardarilli, Gian Carlo Birke, Robert Perri, Stefania |
| author_facet | Silvano, Cristina Ielmini, Daniele Ferrandi, Fabrizio Fiorin, Leandro Curzel, Serena Benini, Luca Conti, Francesco Garofalo, Angelo Zambelli, Cristian Calore, Enrico Schifano, Sebastiano Fabio Palesi, Maurizio Ascia, Giuseppe Patti, Davide Petra, Nicola De Caro, Davide Lavagno, Luciano Urso, Teodoro Cardellini, Valeria Cardarilli, Gian Carlo Birke, Robert Perri, Stefania |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_15552 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms Silvano, Cristina Ielmini, Daniele Ferrandi, Fabrizio Fiorin, Leandro Curzel, Serena Benini, Luca Conti, Francesco Garofalo, Angelo Zambelli, Cristian Calore, Enrico Schifano, Sebastiano Fabio Palesi, Maurizio Ascia, Giuseppe Patti, Davide Petra, Nicola De Caro, Davide Lavagno, Luciano Urso, Teodoro Cardellini, Valeria Cardarilli, Gian Carlo Birke, Robert Perri, Stefania Hardware Architecture Emerging Technologies Machine Learning 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. |
| title | A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms |
| topic | Hardware Architecture Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2306.15552 |