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인공 지능의 열 관리

철도 산업 및 Boyd 솔루션

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

기술이 계속 발전함에 따라 더 많은 기업이 인공 지능(AI)을 활용하여 기존 제품을 개선하고 새로운 솔루션을 생성하여 고객을 위한 가치 창출을 가속화합니다.


떠오르는 인공 지능

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.

인공 지능의 최신 응용

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

성장하는 인공 지능 응용 프로그램

eMobility 및 ADAS 시스템

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

클라우드 컴퓨팅

인공 지능(AI)과 클라우드 컴퓨팅의 결합은 최근 몇 년 동안 디지털 혁신의 주요 원동력이었습니다. 클라우드 컴퓨팅은 조직이 필요에 따라 컴퓨팅 리소스와 스토리지에 액세스할 수 있도록 지원하여 값비싼 하드웨어와 인프라의 필요성을 줄입니다. AI를 클라우드에 통합함으로써 조직은 기계 학습 및 자연어 처리와 같은 AI 기능을 활용하여 비즈니스 운영을 개선하고 프로세스를 자동화하며 의사 결정을 개선합니다. 예를 들어, 기업은 AI 기반 챗봇을 사용하여 고객과 실시간으로 상호 작용하고, 데이터를 분석하여 고객 행동 및 선호도에 대한 통찰력을 얻고, 수요를 예측하여 공급망 관리를 최적화합니다.클라우드 컴퓨팅 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. 커넥티드 의료실


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. 냉각 AI 렌더링

인공 지능의 열 관리

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. 냉각 AI 개념AI가 전력 소모가 심화되고 복잡해짐에 따라 이러한 요구 사항을 충족하면서도 효율적이고 신뢰할 수 있는 냉각 솔루션을 설계하는 것이 중요합니다. 액체 냉각은 특히 소형 폼 팩터와 낮은 전력 소비가 필요한 모바일 AI 시스템의 경우 공기 냉각보다 훨씬 더 높은 냉각 용량을 제공하는 유망한 솔루션입니다.

특정 인공 지능 응용 분야를 위한 열 관리 사용자 지정

인공 지능 냉각 및 열 관리에서 또 다른 중요한 고려 사항은 AI의 특정 적용 및 관련 열 요구 사항입니다. 예를 들어, 자율주행차에 사용되는 AI 시스템은 데이터 센터나 의료 기기에 사용되는 것과 냉각 요구 사항이 다릅니다. 따라서 특정 AI 애플리케이션과 사용 환경에 맞는 냉각 솔루션을 설계하는 것이 필수적입니다.

eMobility 인공 지능을 위한 열 관리

eMobility 애플리케이션의 인공 지능을 위한 열 관리 솔루션은 주로 온보드 전자 장치 및 프로세서, 특히 고급 운전자 지원 시스템의 냉각에 중점을 둡니다. 차량 탑승자의 안전을 높이기 위해 센서 데이터를 수집, 구성, 처리 및 구현하는 시스템은 빠르고 안전한 결정을 내리기 위해 빠르고 안정적인 처리에 의존합니다. eMobility를 위한 냉각 솔루션은 차량 주행 거리와 효율성에 미치는 영향을 최소화하면서 승객의 안전을 보장하기 위해 가볍고 내구성 있는 형식으로 최고의 신뢰성을 필요로 합니다.

의료 및 기업 딥 러닝 및 인공 지능 응용 프로그램 냉각

소비자 또는 의료 애플리케이션을 위한 하이퍼스케일 또는 데이터 센터 환경의 AI 솔루션은 현재 이러한 차세대 열 관리 시스템의 핵심인 냉각수 분배 장치(CDU)를 사용하여 공랭식 솔루션에서 고성능 액체 냉각으로 전환하고 있습니다.

BOYD 차이점

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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