AI Compute – How Chips Drive Next Industrial Leap
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AI Chips Trend Analysis Report Overview
GlobalData’s “AI Compute: How Chips Drive Next Industrial Leap,” innovation radar report explores the transformative impact of semiconductor chip innovations, particularly AI chips, across various industries such as healthcare, telecom, and energy & utilities. The report illuminates how advanced chips are enhancing computational power, enabling improved analytics, and driving automation. By reshaping industrial processes, these technologies are boosting efficiency and fostering innovation.
The “AI Compute” report provides strategic insights to help businesses capitalize on the benefits of advanced chips, optimize operations, and maintain competitiveness in today’s digital world. It serves as a valuable resource for organizations seeking to leverage the potential of semiconductor chips, especially those powering AI applications, to thrive in the modern business environment.
Key Trends | · AI and Machine Learning
· Edge Computing · Advanced Nodes · Quantum Computing · 5G & IoT · Security & Trust · Automotive · Sustainability · System Integration · Supply Chain |
Key Sectors | · Technology
· Healthcare · Automotive · Telecom · Energy & Power |
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Advanced Chip Development – Key Trends
Diverse trends are shaping the semiconductor landscape, spanning from technological shifts such as AI/ML and quantum computing to broader trends such as sustainability and supply chain optimization. A few of the key trends are AI and ML, edge computing, advanced nodes, quantum computing, and 5G & IoT, among others.
AI & Machine Learning: The integration of specialized hardware accelerators for artificial intelligence (AI) and machine learning (ML) workloads enables efficient processing of complex algorithms.
Quantum Computing: Chipmakers invest in the R&D of quantum computing technologies to revolutionize computing capabilities for specific applications.
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Advanced Chips – Sector Impact
Advanced chips are poised to revolutionize various sectors by enhancing performance and energy efficiency in technology, enabling autonomous driving and personalized healthcare, optimizing telecom networks, and improving energy management.
Technology: AI chips enhance tech applications such as data centers, cloud, and edge computing by speeding up AI computations. Moreover, chips optimized for specific tasks can lead to more energy-efficient data centers and computing devices. In the short term, increased adoption of AI chips in data centers will accelerate machine learning tasks while in the medium term, continued advancements in AI chip architectures will improve efficiency and performance.
AI chip startup Tenstorrent positioned itself at the forefront of machine learning innovation with the launch of its Grayskull inference cards. The deliberate focus on inference, supported by the RISC-V architecture and complemented by adaptable software solutions, signals a move towards more specialized, efficient, and accessible AI deployments. As demand for edge-based machine learning solutions continues to rise, the availability of optimized hardware like the Grayskull cards will likely become a key factor in enabling more sophisticated and efficient AI applications.
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Advanced Chips - Patents and Deals
The AI chip R&D landscape is marked by dynamic competition among market players. However, it is geographically concentrated with the US and China being the key hubs of innovation. Multiple players are vying for leadership through innovations in the AI chip patent landscape.
Top 10 Patent Filers, 2019-YTD
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Scope
Innovation Insights: innovation examples by sectors on advanced chips to present key trends.
Key Highlights
Innovations: presents real-world innovation examples related to the advanced chips across different sectors. It casts light on key players across different sectors utilizing advanced chips for their specific applications.
Reasons to Buy
No surprise that technology has been a driving force in business transformation for years, but the term ‘emerging technologies’ has suddenly become the key catalyst to drive the next wave of innovation across sectors. The sense of urgency weighs differently across different sectors, wherein the direct customer-facing sectors are at the forefront compared to other capital-intensive sectors.
- The report helps one understand successful innovations in other sectors to either draw analogies with existing products, services, and processes or transfer strategic approaches for a revolutionary transformation.
- Get an overview of emerging technologies that are impacting sectors and how various companies are implementing them to meet various challenges.
- The innovation landscape report on innovations in advanced chips across sectors, published by GlobalData as part of an ongoing series, covers some real-world examples to advance the development and implementation of the technology by some of the key enterprises as well as startups in various sectors.
Amazon
AmberSemi
Ambiq
AMD
Apple
ASML
Ayar Labs
Celestial AI
CentML
Cerebras
Ceremorphic
CN Bio
D-Matrix
Dell Technologies
Diraq
EdgeQ
Emulate
Enfabrica
Fujitsu
Furiosa
Graphcore
Hailo
IBM
Intel
ISMO Bio-Photonics
Kneron
KT Group
Lightmatter
MediaTek
Meta
Microsoft
Movano
Naver Corp.
Neuralink
NTT
Nvidia
NXP
Obatala
Omnivision
Picocom
POLYN
Portland General Electric
Qualcomm
Rebellions
Renesas
Recogni
Red Hat
Runway AI
SambaNova
Samsung
Sapeon
Schneider Electric
Sima.AI
SK Hynix
Syntiant
Socionext
Tenstorrent
Texas Instruments
Wiliot
Table of Contents
Frequently asked questions
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What are the key trends shaping the advanced chips landscape?
A few of the key trends shaping the advanced chips landscape are AI and ML, edge computing, advanced nodes, quantum computing, and 5G & IoT, among others.
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Which are the key sectors that are impacted by advanced chips?
The implementation of advanced chips is bringing transformation across sectors such as technology, healthcare, automotive, telecom, and energy & power.
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How do advanced chips impact various sectors?
Advanced chips are poised to revolutionize various sectors by enhancing performance and energy efficiency in technology, enabling autonomous driving and personalized healthcare, optimizing telecom networks, and improving energy management.
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