mlsanityData to 14 Oct 2026

Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU

arXiv 2504.03774 ↗First posted 3 Apr 2025cs.DC cs.AI cs.ARcited by 0

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Abstract (arXiv)

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.

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10 of 27 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  4. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  5. 2015Highway Networkscited by 260
  6. 2014Convolutional Neural Networks at Constrained Time Costcited by 81
  7. 2014Rigid-Motion Scattering for Texture Classificationcited by 52
  8. 2023Experimenting with Emerging RISC-V Systems for Decentralised Machine Learningcited by 3
  9. 2023SIMD Everywhere Optimization from ARM NEON to RISC-V Vector Extensionscited by 3
  10. 2024Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkoscited by 1

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