Energy saving in wireless networks is growing in importance due to increasing
demand for evolving new-gen cellular networks, environmental and regulatory
concerns, and potential energy crises arising from geopolitical tensions. In
this work, we propose an approximate dynamic programming (ADP)-based method
coupled with online optimization to switch on/off the cells of base stations to
reduce network power consumption while maintaining adequate Quality of Service
(QoS) metrics. We use a multilayer perceptron (MLP) given each state-action
pair to predict the power consumption to approximate the value function in ADP
for selecting the action with optimal expected power saved. To save the largest
possible power consumption without deteriorating QoS, we include another MLP to
predict QoS and a long short-term memory (LSTM) for predicting handovers,
incorporated into an online optimization algorithm producing an adaptive QoS
threshold for filtering cell switching actions based on the overall QoS
history. The performance of the method is evaluated using a practical network
simulator with various real-world scenarios with dynamic traffic patterns.

利用近似动态规划（ADP）基于在线优化的方法，在维持充分的服务质量（QoS）指标的同时，通过打开 / 关闭基站的方式减少网络功耗。通过使用多层感知器（MLP）来预测功耗和 QoS，以及长短期记忆（LSTM）进行手 over 预测，结合在线优化算法，可以实现适应性 QoS 阈值，以根据整体 QoS 历史记录对基站切换行动进行过滤，并在包含动态流量模式的各种真实场景中使用实际网络模拟器评估该方法的性能。

能效基站小区切换自适应动态规划

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell  Switching

Spiking Neural Network (SNN) as a brain-inspired strategy receives lots of
attention because of the high-sparsity and low-power properties derived from
its inherent spiking information state. To further improve the efficiency of
SNN, some works declare that the Lottery Tickets (LTs) Hypothesis, which
indicates that the Artificial Neural Network (ANN) contains a subnetwork
without sacrificing the performance of the original network, also exists in
SNN. However, the spiking information handled by SNN has a natural similarity
and affinity with binarization in sparsification. Therefore, to further explore
SNN efficiency, this paper focuses on (1) the presence or absence of LTs in the
binary SNN, and (2) whether the spiking mechanism is a superior strategy in
terms of handling binary information compared to simple model binarization. To
certify these consumptions, a sparse training method is proposed to find Binary
Weights Spiking Lottery Tickets (BinW-SLT) under different network structures.
Through comprehensive evaluations, we show that BinW-SLT could attain up to
+5.86% and +3.17% improvement on CIFAR-10 and CIFAR-100 compared with binary
LTs, as well as achieve 1.86x and 8.92x energy saving compared with
full-precision SNN and ANN.

通过使用稀疏训练方法，本文研究了在不同网络结构下找到二进制权重尖峰票（BinW-SLT）是否存在于二进制 SNN 中，以及与简单模型二值化相比，尖峰机制是否在处理二进制信息方面是一种更有效的策略。通过全面评估，我们发现 BinW-SLT 在 CIFAR-10 和 CIFAR-100 上相比于二进制 LTs 提升了 + 5.86% 和 + 3.17%，并且与全精度的 SNN 和 ANN 相比，实现了 1.86 倍和 8.92 倍的能源节省。

在脉冲神经网络中通过探索二进制彩票票券获取稀疏奖励

Gaining the Sparse Rewards by Exploring Binary Lottery Tickets in  Spiking Neural Network

Small devices are frequently used in IoT and smart-city applications to
perform periodic dedicated tasks with soft deadlines. This work focuses on
developing methods to derive efficient power-management methods for periodic
tasks on small devices. We first study the limitations of the existing Linux
built-in methods used in small devices. We illustrate three typical
workload/system patterns that are challenging to manage with Linux's built-in
solutions. We develop a reinforcement-learning-based technique with temporal
encoding to derive an effective DVFS governor even with the presence of the
three system patterns. The derived governor uses only one performance counter,
the same as the built-in Linux mechanism, and does not require an explicit task
model for the workload. We implemented a prototype system on the Nvidia Jetson
Nano Board and experimented with it with six applications, including two
self-designed and four benchmark applications. Under different deadline
constraints, our approach can quickly derive a DVFS governor that can adapt to
performance requirements and outperform the built-in Linux approach in energy
saving. On Mibench workloads, with performance slack ranging from 0.04 s to 0.4
s, the proposed method can save 3% - 11% more energy compared to Ondemand.
AudioReg and FaceReg applications tested have 5%- 14% energy-saving
improvement. We have open-sourced the implementation of our in-kernel quantized
neural network engine. The codebase can be found at:
this https URL

在本研究中，我们专注于为小型设备开发高效的功耗管理方法，通过使用基于强化学习和时间编码的技术从三种具有挑战性的工作负载 / 系统模式中导出一个有效的 DVFS 调度器，该调度器仅使用一个性能计数器并能适应性能需求，在能源节约方面优于内置的 Linux 方法。