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人工智能中的热管理

铁路行业和博伊德解决方案

Last updated Jun 12, 2024 | Published on May 2, 2023

随着技术的不断发展,越来越多的公司利用人工智能 (AI) 来改进现有产品并生成新的解决方案,以加速为客户创造价值。

b2ap3_large_Rising-Artificial-Intelligence

新兴的人工智能

Big tech industry leaders invest heavily in artificial intelligence research and development to enhance search engines, voice assistants, cloud services, and more. These companies also develop innovative AI-based solutions, such as self-driving cars, personalized healthcare, and intelligent virtual assistants. Increasing investment in AI is driving development of new technologies and tools, which are more accessible and affordable to businesses of all sizes, fueling the adoption of AI across industries. As artificial intelligence integrates more with daily life, the increase in processing power and bandwidth increases the need for better and more efficient cooling solutions to ensure the highest performance and reliability for these advanced systems. AI thermal management poses a challenge in the amount of cooling required and the variety of application-specific challenges each industry faces.

人工智能的最新应用

Several studies and reports predict significant growth in the adoption of AI across industries. According to Fortune Business Insights, the global AI market value is expected to reach almost USD 1.4 trillion by 2029, growing at a 20.1% Compound Annual Growth Rate (CAGR) from 2022 to 2029. Increasing AI implementation opens new possibilities for innovation and growth across various industries, such as transportation, healthcare, education, entertainment, and many others. Here are some of the latest AI applications: b2ap3_large_eMobility-and-ADAS-Systems

不断增长的人工智能应用

电动汽车和自动导航系统(ADAS)

eMobility systems and self-driving cars rely on AI to operate safely and efficiently. One of the primary uses of AI in eMobility systems is to manage energy resources, like batteries and charging infrastructure. eMobility systems use AI to optimize charging and discharge cycles to maximize electric vehicle (EV) battery efficiency and lifespan, extend their range, and reduce the cost of ownership. Self-driving cars use AI in Advanced Driver Assist Systems (ADAS) to analyze data from sensors like cameras, lidar, and radar, and to identify and track objects like other vehicles, pedestrians, and obstacles. Self-driving cars also use AI to optimize driving paths, speed, and other parameters based on traffic conditions, road geometry, and user preferences. This enables vehicles to navigate through complex scenarios and make decisions based on real-time data. Integration of AI in the automotive industry has improved the safety and sustainability of transportation systems. Continuous advancement in AI technology enables automotive manufacturers to develop advanced eMobility and self-driving cars that are more efficient, safe, and widely adopted. b2ap3_large_Cloud-Computing

云计算

近年来,人工智能(AI)与云计算的结合一直是数字化转型的主要推动力。云计算可帮助组织按需访问计算资源和存储,从而减少对昂贵硬件和基础架构的需求。通过将 AI 集成到云中,组织可以利用机器学习和自然语言处理等 AI 功能来增强其业务运营、自动化流程并改进决策。例如,公司使用人工智能驱动的聊天机器人与客户实时互动,分析数据以深入了解客户行为和偏好,并通过预测需求来优化供应链管理。云计算 Cloud-based AI providers offer various services like machine learning, speech recognition, image recognition, and natural language processing, making it easier for organizations to integrate AI into their operations. Artificial Intelligence in the cloud offers scalable and cost-effective solutions, enabling businesses to expand their AI capabilities without investing in additional hardware. This is especially beneficial for small and medium-scale enterprises that may not have the resources to invest in hardware infrastructure. The combination of AI and cloud computing has the potential to revolutionize how organizations operate, making them more efficient, effective, and innovative. 连通医疗室

医疗

The use of AI in healthcare is transforming the medical industry globally. Healthcare applies AI to improve the accuracy and speed of medical imaging, diagnose diseases, create treatment plans, speed up the drug discovery process, monitor patients, and provide real-time feedback to doctors. For instance, medical imaging uses AI algorithms to analyze medical images like X-rays, Computed Tomography (CT) scans, and Magnetic Resonance Imaging (MRI) scans to detect abnormalities or signs of disease that may be missed by human interpretation. This helps doctors to make accurate and timely diagnoses, leading to better patient outcomes. Artificial Intelligence also enhances drug discovery process speed by analyzing large datasets and predicting the efficacy of new drug candidates. This helps identify potential treatments more quickly and efficiently, leading to new treatments for different medical conditions. The use of AI in healthcare is revolutionizing healthcare delivery, improving patient outcomes, reducing healthcare costs, speeding up processes, and enabling more personalized care. 冷却 AI 渲染

人工智能中的热管理

As system architects continue to integrate artificial intelligence into various industries and applications, the demand for smaller, more powerful, and energy-efficient computing devices increases. Continued AI adoption means developing more efficient software, hardware, and thermal management solutions to support these demands. In terms of hardware, specialized processors and other components are optimized for artificial intelligence workloads. For example, graphics processing units (GPUs) have been widely adopted in the AI community due to their ability to efficiently perform the matrix operations that are fundamental to many AI algorithms. Chip designers and manufacturers are also developing AI-focused hardware to accelerate deep learning workloads. However, each new hardware development for AI comes with an increased thermal output cost and this thermal demand has quickly outpaced traditional thermal solutions. These chips demand high power to support increased processing demand required for AI tasks. As a result, artificial intelligence hardware generates excessive waste heat that can degrade performance or trigger system failure, which is why AI system designers rely more on thermal management solutions to manage AI processor temperature. 冷却人工智能概念 随着人工智能变得越来越耗电和复杂,设计满足这些需求的冷却解决方案,同时仍然高效可靠,这一点至关重要。液体冷却是一种很有前途的解决方案,它提供比空气冷却高得多的冷却能力,特别是对于需要小尺寸和低功耗的移动AI系统。

为特定人工智能应用定制热管理

人工智能冷却和热管理的另一个重要考虑因素是人工智能的具体应用及其相关的热要求。例如,自动驾驶汽车中使用的人工智能系统与数据中心或医疗设备中使用的人工智能系统具有不同的冷却要求。因此,必须设计针对特定AI应用及其使用环境量身定制的冷却解决方案。

电动汽车人工智能的热管理

电动汽车应用中人工智能的热管理解决方案主要侧重于冷却车载电子设备和处理器,尤其是高级驾驶辅助系统。收集、组织、处理和实施传感器数据以提高车辆乘员安全性的系统依靠快速、可靠的处理来做出快速、安全的决策。电动汽车的冷却解决方案需要以轻便、耐用的形式提供最大的可靠性,以确保乘客安全,同时将对车辆续航里程和效率的影响降至最低。

冷却医疗和企业深度学习和人工智能应用

面向消费或医疗应用的超大规模或数据中心环境中的人工智能解决方案目前正在从风冷解决方案过渡到高性能液体冷却,冷却液分配单元 (CDU) 是这些下一代热管理系统的核心。

博伊德的不同之处

Boyd has decades of experience and expertise designing and manufacturing at scale custom thermal management solutions like coolant distribution units, liquid loops and cold plates, chillers, 3D vapor chambers, and remote heat pipe assemblies for various industries, including eMobility, cloud, medical and more. Our engineering and material science expertise allows us to design a tailored solution for a specific application, whether it is a cooling solution for ADAS systems, data centers, or medical equipment. We leverage our extensive supplier network to obtain the materials and components required to design innovative solutions optimized for performance, reliability, and energy efficiency. To learn more about our thermal management solutions or to discuss your project needs, schedule a consultation with our experts.

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