Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913777713152000 |
|---|---|
| author | Malenza, Giulio Targa, Francesco Garcia, Adriano Marques Aldinucci, Marco Birke, Robert |
| author_facet | Malenza, Giulio Targa, Francesco Garcia, Adriano Marques Aldinucci, Marco Birke, Robert |
| contents | In today's era of rapid technological advancement, artificial intelligence (AI) applications require large-scale, high-performance, and data-intensive computations, leading to significant energy demands. Addressing this challenge necessitates a combined approach involving both hardware and software innovations. Hardware manufacturers are developing new, efficient, and specialized solutions, with the RISC-V architecture emerging as a prominent player due to its open, extensible, and energy-efficient instruction set architecture (ISA). Simultaneously, software developers are creating new algorithms and frameworks, yet their energy efficiency often remains unclear. In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. We specifically analyze the energy consumption of deep learning inference models across three leading AI frameworks: PyTorch, ONNX Runtime, and TensorFlow. Our findings show that frameworks using the XNNPACK back-end, such as ONNX Runtime and TensorFlow, consume less energy compared to PyTorch, which is compiled with the native OpenBLAS back-end. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03774 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU Malenza, Giulio Targa, Francesco Garcia, Adriano Marques Aldinucci, Marco Birke, Robert Distributed, Parallel, and Cluster Computing Artificial Intelligence Hardware Architecture In today's era of rapid technological advancement, artificial intelligence (AI) applications require large-scale, high-performance, and data-intensive computations, leading to significant energy demands. Addressing this challenge necessitates a combined approach involving both hardware and software innovations. Hardware manufacturers are developing new, efficient, and specialized solutions, with the RISC-V architecture emerging as a prominent player due to its open, extensible, and energy-efficient instruction set architecture (ISA). Simultaneously, software developers are creating new algorithms and frameworks, yet their energy efficiency often remains unclear. In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. We specifically analyze the energy consumption of deep learning inference models across three leading AI frameworks: PyTorch, ONNX Runtime, and TensorFlow. Our findings show that frameworks using the XNNPACK back-end, such as ONNX Runtime and TensorFlow, consume less energy compared to PyTorch, which is compiled with the native OpenBLAS back-end. |
| title | Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2504.03774 |