Emerging interface materials for electronics thermal management: experiments, modeling, and new opportunities

2020 ◽  
Vol 8 (31) ◽  
pp. 10568-10586 ◽  
Author(s):  
Ying Cui ◽  
Man Li ◽  
Yongjie Hu
Keyword(s):  
Thermal Management ◽  
High Performance ◽  
State Of The Art ◽  
Interface Materials

State-of-the-art experiments and modeling, challenges, and future opportunities for developing high-performance interface materials for electronics thermal management.

Thermal Management of Fujitsu High-End Unix Servers

2005 ◽  
Author(s):  
Jie Wei ◽  
Masahiro Suzuki
Keyword(s):  
Thermal Management ◽  
High Performance ◽  
Thermal Interface Materials ◽  
Air Cooling ◽  
Asymmetric Power ◽  
High Performance Computers ◽  
Interface Materials ◽  
Heat Spreading ◽  
System Packaging ◽  
Processor Module

An overview of system packaging and thermal management of the Fujitsu high-performance Unix server PRIMEPOWER HPC2500 is introduced in the present paper. Each node of the HPC2500 comprises sixteen system boards containing up to 128 SPARC64V CPU processors, with the maximum power dissipation about 30kW. A full configuration of the HPC2500 can be further scaled up to the maximum of 128 nodes, resulting in 16,384 CPU processors installed. Thermal management is introduced from viewpoints of the server-cabinet, system-board and CPU processor module levels, respectively. A design strategy of the forced convection air cooling scheme is outlined, implemented to meet increased demanding requirements of high density packaging, high power and power-density dissipations. Furthermore, thermal challenges, arising from high asymmetric power distributions and dissipations of CPU processors, are investigated for high performance computers. Temperature distributions of the CPU processors, effects of heat spreading materials (HIS) and impacts of thermal interface materials (TIM) on the cooling and packaging designs are also analysed and illustrated.   This paper was also originally published as part of the Proceedings of the ASME 2005 Heat Transfer Summer Conference.

scholarly journals Recent Advances in Thermal Metamaterials and Their Future Applications for Electronics Packaging

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jae Choon Kim ◽  
Zongqing Ren ◽  
Anil Yuksel ◽  
Ercan M. Dede ◽  
Prabhakar R. Bandaru ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Thermal Management ◽  
High Performance ◽  
State Of The Art ◽  
Electronics Packaging ◽  
The State ◽  
Future Research ◽  
Conductive Heat ◽  
Recent Advances ◽  
Conventional Cooling

Abstract Thermal metamaterials exhibit thermal properties that do not exist in nature but can be rationally designed to offer unique capabilities of controlling heat transfer. Recent advances have demonstrated successful manipulation of conductive heat transfer and led to novel heat guiding structures such as thermal cloaks, concentrators, etc. These advances imply new opportunities to guide heat transfer in complex systems and new packaging approaches as related to thermal management of electronics. Such aspects are important, as trends of electronics packaging toward higher power, higher density, and 2.5D/3D integration are making thermal management even more challenging. While conventional cooling solutions based on large thermal-conductivity materials as well as heat pipes and heat exchangers may dissipate the heat from a source to a sink in a uniform manner, thermal metamaterials could help dissipate the heat in a deterministic manner and avoid thermal crosstalk and local hot spots. This paper reviews recent advances of thermal metamaterials that are potentially relevant to electronics packaging. While providing an overview of the state-of-the-art and critical 2.5D/3D-integrated packaging challenges, this paper also discusses the implications of thermal metamaterials for the future of electronic packaging thermal management. Thermal metamaterials could provide a solution to nontrivial thermal management challenges. Future research will need to take on the new challenges in implementing the thermal metamaterial designs in high-performance heterogeneous packages to continue to advance the state-of-the-art in electronics packaging.

scholarly journals Flexible thermal interface based on self-assembled boron arsenide for high-performance thermal management

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ying Cui ◽  
Zihao Qin ◽  
Huan Wu ◽  
Man Li ◽  
Yongjie Hu
Keyword(s):  
Thermal Management ◽  
High Performance ◽  
Rational Design ◽  
State Of The Art ◽  
Biological Tissues ◽  
Great Promise ◽  
Thermal Interface ◽  
Current State ◽  
Boron Arsenide ◽  
Self Assembled

AbstractThermal management is the most critical technology challenge for modern electronics. Recent key materials innovation focuses on developing advanced thermal interface of electronic packaging for achieving efficient heat dissipation. Here, for the first time we report a record-high performance thermal interface beyond the current state of the art, based on self-assembled manufacturing of cubic boron arsenide (s-BAs). The s-BAs exhibits highly desirable characteristics of high thermal conductivity up to 21 W/m·K and excellent elastic compliance similar to that of soft biological tissues down to 100 kPa through the rational design of BAs microcrystals in polymer composite. In addition, the s-BAs demonstrates high flexibility and preserves the high conductivity over at least 500 bending cycles, opening up new application opportunities for flexible thermal cooling. Moreover, we demonstrated device integration with power LEDs and measured a superior cooling performance of s-BAs beyond the current state of the art, by up to 45 °C reduction in the hot spot temperature. Together, this study demonstrates scalable manufacturing of a new generation of energy-efficient and flexible thermal interface that holds great promise for advanced thermal management of future integrated circuits and emerging applications such as wearable electronics and soft robotics.

Advanced Thermal Interface Materials for Thermal Management

2018 ◽  
Author(s):  
Wei Yu ◽  
◽  
Changqing Liu ◽  
Lin Qiu ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Thermal Management ◽  
Thermal Interface Materials ◽  
Thermal Interface ◽  
Interface Materials

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

scholarly journals Communication Failure Resilient Distributed Neural Network for Edge Devices

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1614
Author(s):  
Jonghun Jeong ◽  
Jong Sung Park ◽  
Hoeseok Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
State Of The Art ◽  
Wearable Devices ◽  
Communication Failure ◽  
Canadian Institute ◽  
Multiple Devices ◽  
Knowledge Distillation ◽  
Partitioning Technique

Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single device. Instead, it has been proposed to run a single NN application cooperatively on top of multiple devices, a so-called distributed neural network. In the distributed neural network, workloads of a single big NN application are distributed over multiple tiny devices. While the computation overhead could effectively be alleviated by this approach, the existing distributed NN techniques, such as MoDNN, still suffer from large traffics between the devices and vulnerability to communication failures. In order to get rid of such big communication overheads, a knowledge distillation based distributed NN, called Network of Neural Networks (NoNN), was proposed, which partitions the filters in the final convolutional layer of the original NN into multiple independent subsets and derives smaller NNs out of each subset. However, NoNN also has limitations in that the partitioning result may be unbalanced and it considerably compromises the correlation between filters in the original NN, which may result in an unacceptable accuracy degradation in case of communication failure. In this paper, in order to overcome these issues, we propose to enhance the partitioning strategy of NoNN in two aspects. First, we enhance the redundancy of the filters that are used to derive multiple smaller NNs by means of averaging to increase the immunity of the distributed NN to communication failure. Second, we propose a novel partitioning technique, modified from Eigenvector-based partitioning, to preserve the correlation between filters as much as possible while keeping the consistent number of filters distributed to each device. Throughout extensive experiments with the CIFAR-100 (Canadian Institute For Advanced Research-100) dataset, it has been observed that the proposed approach maintains high inference accuracy (over 70%, 1.53× improvement over the state-of-the-art approach), on average, even when a half of eight devices in a distributed NN fail to deliver their partial inference results.

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