Journal of Information and Intelligence最新文献

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An efficient machine learning-enhanced DTCO framework for low-power and high-performance circuit design 一种高效的机器学习增强DTCO框架，用于低功耗和高性能电路设计

Journal of Information and Intelligence

Pub Date : 2025-05-01 DOI: 10.1016/j.jiixd.2025.02.001

Mingyang Liu , Zhengguang Tang , Hailong You , Cong Li , Guangxin Guo , Zeyuan Wang , Linying Zhang , Xingming Liu , Yu Wang , Yong Dai , Geng Bai , Xiaoling Lin

The standard design technology co-optimization (DTCO) involves frequent interactions between circuit design and process manufacturing, which requires several months. To assist designers in establishing a bridge between device parameters and circuit metrics efficiently, and provide guidance for parameter optimization in the early stages of circuit design. In this paper, we propose an efficient machine learning (ML)-enhanced DTCO framework. This framework achieves the co-optimization of device parameters and circuit metrics. We select the gate metal work function (WF) as the parameter to validate the effectiveness of our framework. And the ridge regression approach is used to bypass TCAD simulation, compact model extraction and cell library characterization. We reduces time consumption by at least 92% compared to traditional DTCO framework, while ensuring that errors of delay, internal power consumption and leakage power below 4 ps, 0.035 mJ, and 0.4 μW, respectively. By adjusting the WF, we achieved a better balance between circuit delay and power consumption. This work contributes to designers exploring a broader design space and achieving a efficient DTCO flow.

标准设计技术协同优化（DTCO）涉及电路设计和工艺制造之间的频繁交互，需要数月的时间。协助设计人员有效地在器件参数和电路指标之间建立桥梁，为电路设计初期的参数优化提供指导。在本文中，我们提出了一个高效的机器学习（ML）增强的DTCO框架。该框架实现了器件参数和电路指标的协同优化。我们选择闸门金属功函数（WF）作为参数来验证框架的有效性。脊回归方法可以绕过TCAD仿真、紧凑模型提取和细胞库表征。与传统的DTCO框架相比，我们将时间消耗降低了至少92%，同时确保延迟、内部功耗和泄漏功率的误差分别低于4 ps、0.035 mJ和0.4 μW。通过调整WF，我们在电路延迟和功耗之间取得了更好的平衡。这项工作有助于设计师探索更广阔的设计空间，实现高效的DTCO流程。

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引用次数: 0

Resource allocation for coexistence of eMBB and bursty URLLC based on queueing with preemption 基于抢占排队的eMBB和突发URLLC共存资源分配

Journal of Information and Intelligence

Pub Date : 2025-05-01 DOI: 10.1016/j.jiixd.2025.03.003

Wei Guo , Kai Liang , Yuewen Song , Xiaoli Chu , Gan Zheng , Kai-Kit Wong

Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) are two critical services in 5G mobile networks. While there has been extensive research on their coexistence, few studies have considered the impact of bursty URLLC on their coexistence performance. In this paper, we propose a method to allocate computing and radio resources for coexisting eMBB and bursty URLLC services by preempting both computing queues in the base station (BS) and time-frequency resources at the air interface. Specifically, we first divide the computing resources at the BS into a shared part for both URLLC and eMBB users and an exclusive part only for eMBB users, and propose a queuing mechanism with preemptive-resume priority for accessing the shared computing resources. Furthermore, we propose a preemptive puncturing method and a threshold-based queuing mechanism in the air interface to enable the multiplexing of eMBB and URLLC on shared time-frequency resources. We analytically derive the average queuing delay, average computation delay, and average transmission delay of eMBB and URLLC packets. Based on this analysis, we formulate a mixed-integer nonlinear programming problem to minimize the average delay of URLLC packets while satisfying the average delay and throughput requirements of eMBB by jointly optimizing the eMBB subcarrier allocation, the URLLC subcarrier scheduling and the computing resource allocation. We decompose this problem into three sub-problems and solve them alternately using a block coordinate descent algorithm. Numerical results show that our proposed method reduces the outage probability and average delay of URLLC compared to the existing works.

增强型移动宽带（eMBB）和超可靠低延迟通信（URLLC）是5G移动网络中的两项关键业务。虽然对它们的共存进行了大量的研究，但很少有研究考虑突发URLLC对它们共存性能的影响。本文提出了一种通过抢占基站（BS）的计算队列和空中接口的时频资源，为同时存在的eMBB和突发URLLC业务分配计算和无线电资源的方法。具体而言，我们首先将BS的计算资源划分为URLLC和eMBB用户共享的部分和eMBB用户独占的部分，并提出了一种具有抢占-恢复优先级的访问共享计算资源的排队机制。此外，我们提出了一种先发制人的穿刺方法和基于阈值的空中接口排队机制，以实现eMBB和URLLC在共享时频资源上的复用。分析了eMBB和URLLC包的平均排队延迟、平均计算延迟和平均传输延迟。在此基础上，通过对eMBB子载波分配、URLLC子载波调度和计算资源分配进行联合优化，提出了在满足eMBB平均时延和吞吐量要求的同时最小化URLLC数据包平均时延的混合整数非线性规划问题。我们将该问题分解为三个子问题，并使用块坐标下降算法交替求解。数值计算结果表明，与现有工程相比，该方法降低了URLLC的中断概率和平均延迟。

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引用次数: 0

Multimodal emotion recognition method in complex dynamic scenes 复杂动态场景中的多模态情感识别方法

Journal of Information and Intelligence

Pub Date : 2025-05-01 DOI: 10.1016/j.jiixd.2025.02.004

Long Liu , Qingquan Luo , Wenbo Zhang , Mengxuan Zhang , Bowen Zhai

Multimodal emotion recognition technology leverages the power of deep learning to address advanced visual and emotional tasks. While generic deep networks can handle simple emotion recognition tasks, their generalization capability in complex and noisy environments, such as multi-scene outdoor settings, remains limited. To overcome these challenges, this paper proposes a novel multimodal emotion recognition framework. First, we develop a robust network architecture based on the T5-small model, designed for dynamic-static fusion in complex scenarios, effectively mitigating the impact of noise. Second, we introduce a dynamic-static cross fusion network (D-SCFN) to enhance the integration and extraction of dynamic and static information, embedding it seamlessly within the T5 framework. Finally, we design and evaluate three distinct multi-task analysis frameworks to explore dependencies among tasks. The experimental results demonstrate that our model significantly outperforms other existing models, showcasing exceptional stability and remarkable adaptability to complex and dynamic scenarios.

多模态情感识别技术利用深度学习的力量来解决高级视觉和情感任务。虽然通用深度网络可以处理简单的情绪识别任务，但它们在复杂和嘈杂环境（如多场景户外环境）中的泛化能力仍然有限。为了克服这些挑战，本文提出了一种新的多模态情感识别框架。首先，我们开发了基于T5-small模型的鲁棒网络架构，设计用于复杂场景下的动态-静态融合，有效减轻噪声的影响。其次，我们引入了一种动态-静态交叉融合网络（D-SCFN）来增强动态和静态信息的集成和提取，并将其无缝嵌入到T5框架中。最后，我们设计并评估了三个不同的多任务分析框架，以探索任务之间的依赖关系。实验结果表明，我们的模型明显优于其他现有模型，表现出优异的稳定性和对复杂和动态场景的卓越适应性。

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