According to a report from Wall Street Journal, Alphabet CFO Ruth Porat announced in a statement on May 30 that Google has committed to investing USD 2 billion in Malaysia. The investment includes building its first data center, expanding Google Cloud, and further developing artificial intelligence (AI).

Porat highlighted that this will be Google’s largest investment project in Malaysia. Google estimates that this investment will contribute over USD 3.2 billion to Malaysia’s GDP and create 26,500 jobs by 2030.

As per a report from Bloomberg, Google stated that in addition to developing cloud computing services, it will also support AI literacy programs for students and educators.

In its earnings call in April, Porat mentioned that the significant year-over-year increase in capital expenditures over recent quarters reflects Alphabet’s confidence in the potential of AI. She projected that the quarterly capital expenditures for the second to fourth quarters of this year would be comparable to or slightly higher than those in the first quarter.

On May 2, Microsoft Corp. announced that it will invest USD 2.2 billion in Malaysia over the next four years to support the country’s digital transformation. The investment projects include developing digital infrastructure, creating AI skill opportunities, establishing a National AI Excellence Center, and enhancing Malaysia’s cybersecurity capabilities.

Earlier this week, Malaysian Prime Minister Anwar Ibrahim announced the National Semiconductor Strategy, which includes providing at least USD 5.3 billion in financial support and training 60,000 semiconductor engineers, aiming to make Malaysia a global chip hub.

Amidst the U.S.-China rivalry and other geopolitical tensions, global companies are seeking to diversify their supply chains. Facing competition between the U.S. and China, Malaysia is reportedly keen to maintain a neutral position in the semiconductor supply chain landscape. According to the Malaysian Investment Development Authority (MIDA), the country currently provides 13% of global testing and packaging.

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• [News] Malaysia’s Major Investment Aims to Establish Global Chip Hub
• [News] Overseas Expansion of Testing and Packaging Facilities – Japan, Malaysia, and Singapore Emerge as Top Choices

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As a strategic technology empowering a new round of technological revolution and industrial transformation, AI has become one of the key driving forces for the development of new industrialization. Fueled by the ChatGPT craze, AI and its applications are rapidly gaining traction worldwide. From an industrial perspective, NVIDIA currently holds almost absolute dominance in the AI chip market.

Meanwhile, major tech companies such as Google, Microsoft, and Apple are actively joining the competition, scrambling to seize the opportunity. Meta, Google, Intel, and Apple have launched the latest AI chips in hopes of reducing reliance on companies like NVIDIA. Microsoft and Samsung have also reportedly made investment plans for AI development.

• Microsoft Announced an Investment Plan of over USD 11 Billion

Recently, according to multiple global media reports, Microsoft is developing a new AI mega-model called MAI-1. This model far exceeds some of Microsoft’s previously released open-source models in scale and is expected to rival well-known large models like Google’s Gemini 1.5, Anthropic’s Claude 3, and OpenAI’s GPT-4 in terms of performance. Reports suggest that Microsoft may demonstrate MAI-1 at the upcoming Build developer conference.

In response to the growing demand for AI computing, Microsoft recently announced a plan to invest billions of dollars in building AI infrastructure in Wisconsin. Microsoft stated that this move will create 2,300 construction jobs, and could contribute to up to 2,000 data center jobs when completing construction.

Furthermore, Microsoft will establish a new AI lab at the University of Wisconsin-Milwaukee to provide AI technology training.

Microsoft’s investment plan in the US involves an amount of USD 3.3 billion, which plus its investments previously announced in Japan, Indonesia, Malaysia and Thailand amount to over USD 11 billion in reference to AI-related field.

Microsoft’s recent announcements shows that it plans to invest USD 2.9 billion over the next two years to enhance its cloud computing and AI infrastructure in Japan, USD 1.7 billion within the next four years to expand cloud services and AI in Indonesia, including building data centers, USD 2.2 billion over the next four years in Malaysia in cloud computing and AI, and USD 1 billion to set up the first data center in Thailand, dedicated to providing AI skills training for over 100,000 people.

• Apple Reportedly Developing in-House AI Chip for Data Center

Apple has also unveiled its first AI chip, M4. Apple introduced that the neural engine in M4 chip is the most powerful one the company has ever developed, outstripping any neural processing unit in current AI PCs. Apple further emphasized that it will “break new ground” in generative AI this year, bringing transformative opportunities to users.

According to a report from The Wall Street Journal, Apple has been working on its own chips designed to run AI software on data center servers. Sources cited in the report revealed that the internal codename for the server chip project is ACDC (Apple Chips in Data Center). The report indicates that the ACDC project has been underway for several years, but it’s currently uncertain whether this new chip will be commissioned and when it might hit the market.

Tech journalist Mark Gurman also suggests that Apple will introduce AI capabilities in the cloud this year using its proprietary chips. Gurman’s sources indicate that Apple intends to deploy high-end chips (Similar to those designed for Mac) in cloud computing servers to handle cutting-edge AI tasks on Apple devices. Simpler AI-related functions will continue to be processed directly by chips embedded in iPhone, iPad, and Mac devices.

• Samsung might Start Pilot production for AI Inference Chip Mach-1

As per industry sources cited by South Korean media outlet ZDNet Korea, Samsung Electronics’ AI inference chip, Mach-1, is set to begin prototype production using a multi-project wafer (MPW) approach and is expected to be based on Samsung’s in-house 4nm process.

Previously at a shareholder meeting, Samsung revealed its plan to launch a self-made AI accelerator chip, Mach-1, in early 2025. As a critical step in Samsung’s AI development strategy, Mach-1 chip is an AI inference accelerator built on application-specific integrated circuit (ASIC) design and equipped with LPDDR memory, making it particularly suitable for edge computing applications.

Kyung Kye-hyun, head of Samsung Electronics’ DS (Semiconductor) division, stated that the development goal of this chip is to reduce the data bottleneck between off-chip memory and computing chips to 1/8 through algorithms, while also achieving an eight-fold improvement in efficiency. He noted that Mach-1 chip design has gained the verification of field-programmable gate array (FPGA) technology and is currently in the physical implementation stage of system-on-chip (SoC), which is expected to be ready in late 2024, with a Mach-1 chip-driven AI system to be launched in early 2025.

In addition to developing AI chip Mach-1, Samsung has established a dedicated research lab in Silicon Valley focusing on general artificial intelligence (AGI) research. The intention is to develop new processors and memory technologies capable of meeting future AGI system processing requirements.

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• [News] Microsoft to Build AI Data Center at Former Foxconn Planned Panel Plant Site in Wisconsin
• [News] Apple Allegedly Developing AI Processor for Data Centers, with TSMC as Its Foundry Partner

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Microsoft President Brad Smith announced the investment of USD 3.3 billion to construct an artificial intelligence data center in Wisconsin, aiming to make the state a core driver of the innovation economy. Notably, the site of the facility was originally intended for a LCD panel plant promised by Foxconn six years ago.

According to Microsoft’s press release, the AI data center in Wisconsin is expected to create 2,300 union construction opportunities by 2025 and will provide long-term employment opportunities over the next several years.

Microsoft’s press release highlights that this investment will be utilized for constructing cloud computing and artificial intelligence infrastructure, establishing the first AI co-innovation lab in the United States focused on the manufacturing industry, and promoting AI training programs with the goal of enabling over 100,000 Wisconsin residents to acquire necessary AI skills.

The press release also notes that Microsoft will collaborate with Gateway Technical College to establish a Data Center Academy, aiming to train more than 1,000 students within five years, equipping them to enter roles in data centers or information technology departments.

Microsoft’s new facility in Racine County, Wisconsin, was originally intended to be the site of a LCD panel plant planned by Foxconn, a subsidiary of Hon Hai Precision Industry Co., Ltd. (Foxconn Group), according to a report by CNA.

In June 2018, then-chairman of Foxconn, Terry Gou, and then-US President Donald Trump attended the groundbreaking ceremony for the panel plant. Foxocnn announced an investment of USD 10 billion, and Trump described the project as the “8th wonder of the world.”

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• [News] Microsoft Reportedly Stockpiling AI Chips to Boost Hardware Capability
• [News] Breaking Away from NVIDIA Dependency, Microsoft Reportedly Developing In-House AI Server High-Speed Network Card

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The digital world is undergoing a massive transformation powered by the convergence of two major trends: an insatiable demand for real-time insights from data, and the rapid advancement of Generative artificial intelligence (AI). Leaders like Amazon, Microsoft, and Google are in a high-stakes race to deploy Generative AI to drive innovation. Bloomberg Intelligence predicts that the Generative AI market will grow at a staggering 42% year over year in the next decade, from $40 billion in 2022 to $1.3 trillion.

Meanwhile, this computational force is creating a massive surge in energy demand—posing serious consequences for today’s data center operators. Current power conversion and distribution technologies in the data center can’t handle the increase in demand posed by the cloud and machine learning—and certainly not from power-hungry Generative AI applications. The quest for innovative data center solutions has never been more critical.

Gallium Nitride (GaN) semiconductors emerge as a pivotal solution to data center power concerns, helping counter the impact of Generative AI challenges. We dive into how Generative AI affects data centers, the advantages GaN, and a prevailing industry perception of the Power Usage Effectiveness (PUE) metric—which is creating headwinds despite GaN’s robust adoption. With Generative AI intensifying power demands, swift measures are essential to reshape this perception and propel GaN adoption even further.

The rising impact of Generative AI on the data center

Today’s data center infrastructure, designed for conventional workloads, is already strained to its limits. Meanwhile, the volume of data across the world doubles in size every two years—and the data center servers that store this ever-expanding information require vast amounts of energy and water to operate. McKinsey projects that the U.S. alone will see 39 gigawatts of new data center demand, about 32 million homes’ worth, over the next five years.

The energy-intensive nature of generative AI is compounding the data center power predicament. According to one research article, the recent class of generative AI models requires a ten to a hundred-fold increase in computing power to train models over the previous generation. Generative AI applications create significant demand for computing power in two phases: training the large language models (LLMs) that form the core of generative AI systems, and then operating the application with these trained LLMs.

If you consider that a single Google search has the potential to power a 100W lightbulb for 11 seconds, it’s mind-boggling to think that one ChatGPT AI session consumes 50 to 100 times more energy than a similar Google search. Data centers are not prepared to handle this incredible surge in energy consumption. One CEO estimates that $1 trillion will be spent over the next four years upgrading data centers for AI.

Unfortunately, while technologies like immersion cooling, AI-driven optimizations, and waste heat utilization have emerged, they offer only partial solutions to the problem. A critical need exists for power solutions that combine high efficiency, compact form factors, and deliver substantial power outputs. Power electronics based on silicon are inefficient, requiring data centers to employ cooling systems to maintain safe temperatures.

GaN: Unparalleled performance and efficiency

GaN offers unparalleled performance and efficiency compared to traditional power supply designs, making it an ideal option for today’s data centers—particularly as Generative AI usage escalates. GaN transistors can operate at faster switching speeds and have superior input and output figures-of-merit. These features translate into system benefits including higher operating efficiency, exceeding Titanium, and increased power density.

GaN transistors enable data center power electronics to achieve higher efficiency levels—curbing energy waste and generating significantly less heat. The impact is impressive. In a typical data center environment, each cluster of ten racks powered by GaN transistors can result in a yearly profit increase of $3 million, a reduction of 100 metric tons of CO2 emissions annually, and a decrease in OPEX expenses by $13,000 per year. These benefits will only increase as the power demands of Generative AI increase and rack power density rises 2-3X.

While the benefits of GaN are profound, why aren’t even more data center operators swiftly incorporating the technology? Adoption faces headwinds from what we call the “PUE loophole”—an often-overlooked weakness within the widely accepted PUE metric.

The PUE Loophole

The PUE metric is the standard tool for assessing data center energy efficiency, calculated by dividing the total facility power consumption by the power utilized by IT equipment. The metric helps shape data center operations and guides efforts to reduce energy consumption, operational costs, and environmental impact.

Data center operators continuously strive to monitor and improve the PUE to indicate reduced energy consumption, carbon emissions, and associated costs. However, the PUE metric measures how efficiently power is delivered to servers—yet it omits power conversion efficiency within the server itself. As a result, the PUE calculation does not provide a comprehensive view of the energy efficiency within a data center—creating a blind spot for data center operators.

Consider that many servers still use AC/DC converters that are 90 percent efficient or less. While this may sound impressive—10 percent or more of all energy in a data center is lost. This not only increases costs and CO2 emissions, but it also creates extra waste heat, putting additional demands on cooling systems.

GaN is remarkably effective in addressing the PUE Loophole. For instance, the latest generation of GaN-based server AC/DC converters are 96 percent efficient or better – which means that more than 50 percent of the wasted energy can instead be used effectively. Across the entire industry, this could translate into more than 37 billion kilowatt-hours saved every year—enough to run 40 hyperscale data centers.

GaN can provide an immediately cost-effective way to close the PUE loophole and save high amounts of energy. But because the PUE doesn’t consider AC/DC conversion efficiency in the server, there is no incentive to make AC/DC converters more efficient.

This article was authored by Paul Wiener, Vice President of Strategic Marketing at GaN Systems.

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According to TrendForce research, the global high-performance computing market reached approximately US$36.8 billion in 2021, growing 7.1% compared to 2020. The United States is still the largest market for high-performance computing in the world with an approximate 48% share, followed by China and Europe, with a combined share of approximately 35%. Segregated into application markets, high-performance computing is most widely used in scientific research, national defense/government affairs, and commercial applications, with market shares of 15%, 25%, and 50%, respectively. In terms of product type, software (including services) and hardware account for 58% and 42% of the market, respectively.

Since high-performance computing can support data analysis, machine learning (ML), network security, scientific research, etc., it plays a key role in military fields such as nuclear warhead design and missile explosion simulations. Therefore, there are relatively few players occupying key positions in the value chain. Primary suppliers are Fujitsu, HPE, Lenovo, and IBM. These four manufacturers account for a market share of approximately 73.5% globally.

In addition, the continuous development of smart cities, smart transportation, self-driving cars, the metaverse, and space exploration and travel programs launched by Space X, Blue Origin, and Virgin Galactic will increase the demand for high-performance computing focused on R&D and testing along the two major axes of simulation and big data processing and analysis. The global high-performance computing market is expected to reach US$39.7 billion in 2022, with a growth rate of 7.3%. The CAGR (Compound Annual Growth Rate) of the global high-performance computing market from 2022 to 2027 will be 7.4%.

In view of this, the global high-performance computing market is growing steadily but not by much. The reason is that many of the aforementioned commercial application terminals are still in the growth stage, so high-performance computing technologies and solutions adopted by cloud service providers are limited to local deployment This enables HPC servers to scale on-premises or in the cloud and provides dedicated storage systems and software to drive innovation, thereby accelerating the development of hybrid HPC solutions.

In terms of end-use, the high-performance computing market is segmented into BFSI (Banking, Financial Services and Insurance), manufacturing, healthcare, retail, transportation, gaming, entertainment media, education & research, and government & defense. High-performance computing’s highest revenue share was derived from the government and defense market in 2021, primarily due to related agencies actively adopting cutting-edge and advanced IT solutions to improve computing efficiency. At present, government agencies in the United States, China, Japan, South Korea, as well as European countries have successively adopted high-performance computing systems to support digitization projects and contribute to economic development. Therefore, in 2021, the global scale of the on-premise high-performance computing server market was US$14.8 billion, of which Supercomputer, Divisional, Departmental, and Workgroup accounted for 46.6%, 18.9%, 25%, and 9.5% of the market, respectively. The global on-premise high-performance computing server market in 2022 is expected to reach US$16.7 billion with Supercomputer and Divisional growing by 11.5% and 15.2% compared with 2021.

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