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Energy Efficiency in AI for 5G and Beyond: A DeepRx Case Study

arXiv 2507.10409 ↗First posted 14 Jul 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

This study addresses the challenge of balancing energy efficiency with performance in AI/ML models, focusing on DeepRX, a deep learning receiver based on a fully convolutional ResNet architecture. We evaluate the energy consumption of DeepRX, considering factors including FLOPs/Watt and FLOPs/clock, and find consistency between estimated and actual energy usage, influenced by memory access patterns. The research extends to comparing energy dynamics during training and inference phases. A key contribution is the application of knowledge distillation (KD) to train a compact DeepRX student model that emulates the performance of the teacher model but with reduced energy consumption. We experiment with different student model sizes, optimal teacher sizes, and KD hyperparameters. Performance is measured by comparing the Bit Error Rate (BER) performance versus Signal-to-Interference & Noise Ratio (SINR) values of the distilled model and a model trained from scratch. The distilled models demonstrate a lower error floor across SINR levels, highlighting the effectiveness of KD in achieving energy-efficient AI solutions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 14 Jul 2025.

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References · 11

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 434
  3. 2019On the Efficacy of Knowledge Distillationcited by 381
  4. 2021Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Bettercited by 154
  5. 2016Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruningcited by 134
  6. 2022Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Trainingcited by 79
  7. 2021Compute and Energy Consumption Trends in Deep Learning Inferencecited by 61
  8. 2020DeepRx: Fully Convolutional Deep Learning Receivercited by 52
  9. 2018ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multiplierscited by 42
  10. 2022Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Modelcited by 7
  11. 2022AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networkscited by 4

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