标题： 人工智能在5G及未来中的能效：一个DeepRx案例研究 显示英文标题

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

摘要： 本研究解决了在AI/ML模型中平衡能效与性能的挑战，重点关注基于全卷积ResNet架构的深度学习接收器DeepRX。 我们评估了DeepRX的能耗，考虑了包括FLOPs/Watt和FLOPs/clock等因素，并发现估算的能耗与实际能耗之间具有一致性，这受到内存访问模式的影响。 研究扩展到比较训练和推理阶段的能耗动态。 一个关键贡献是将知识蒸馏（KD）应用于训练一个紧凑的DeepRX学生模型，该模型能够模仿教师模型的性能，但能耗更低。 我们尝试了不同的学生模型大小、最佳教师模型大小和KD超参数。 性能通过比较蒸馏模型和从零开始训练的模型的比特错误率（BER）性能与信干噪比（SINR）值来衡量。 蒸馏模型在不同SINR水平下表现出更低的误差地板，突显了KD在实现节能AI解决方案方面的有效性。 显示英文摘要

摘要： 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.