Japan’s leading NAND flash manufacturer, Kioxia, has reportedly increased its production line utilization rate to 100% as of June and is set to commence mass production of its most advanced NAND flash products in July.

According to a report from Nikkei on July 3rd, Kioxia will start mass-producing its latest NAND flash products at its Yokkaichi plant in July. This move is said to be meeting the rapidly growing data storage demands driven by the proliferation of generative AI. Reportedly, the new NAND flash products Kioxia will produce feature 218-layer 3D flash, offering approximately 50% more storage capacity and requiring about 30% less power for data writing compared to current products.

Per the same report, besides the increasing demand driven by AI, the improvement in the memory market also make Kioxia’s production line utilization rate to return to 100% in June. Previously, Kioxia had been implementing production cuts since October 2022 due to sluggish demand for smartphones, with the reduction scale exceeding 30% at its peak.

In an earlier report from The Register, Kioxia announced a partnership with Western Digital (WD) to invest JPY 729 billion in mass-producing advanced memory products. The new plant, located in the Kitakami plant area, is scheduled to start operations in 2025. The Japanese Ministry of Economy, Trade and Industry will provide a subsidy of up to JPY 243 billion (roughly USD 1.63 billion).

In April 2017, Toshiba spun off its semiconductor business focused on NAND Flash, creating “Toshiba Memory.” This entity was renamed “Kioxia” on October 1, 2019. Toshiba currently holds approximately 40% of Kioxia’s shares.

According to another Reuters’ report on June 26th citing sources,  they have indicated that Kioxia will soon submit an initial application for listing on the Tokyo Stock Exchange, aiming for an IPO by the end of October. Kioxia had been evaluating the possibility of going public to raise funds, and with the recovery of the semiconductor market and a rapid improvement in performance, it has determined that the timing is favorable for an IPO.

The sources cited by Reuters further indicate that Kioxia plans to submit its formal IPO application by the end of August, with the goal of going public by the end of October. To meet this timeline, preparations are being carried out at a faster pace than usual for an IPO. However, depending on progress, the IPO could be delayed until December. The sources also noted that Kioxia’s major shareholder, the American investment fund Bain Capital, plans to sell part of its stake during the IPO to recoup some of its investment.

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(Photo credit: Kioxia)

As the Apple Worldwide Developers Conference (WWDC) in June approaches, recent rumors about Apple’s AI research have resurfaced. According to reports from MacRumors and Tom’s Guide, Apple is reportedly developing a large language model (LLM) comparable to ChatGPT that can run directly on devices without relying on cloud platforms.

In late February of this year, Apple reportedly decided to terminate its electric car development project “Project Titan” initiated a decade ago and redirected research funds and resources into the field of generative AI. This move has drawn significant attention to Apple’s activities in the AI sector.

Moreover, MacRumors also reports that Apple’s AI research team, led by John Giannandrea, began developing a conversational AI software, known today as a large language model, four years ago. It is understood that Apple’s proprietary large language model has been trained with over 200 billion parameters, making it more powerful than ChatGPT 3.5.

Previously, Apple disclosed that the iOS 18 operating system, set to launch this year, will incorporate AI capabilities. Recently, tech website Tom’s Guide speculated further that iOS 18 could execute large language models directly on Apple devices. However, whether Apple’s large language model can be successfully integrated into various Apple software services remains to be seen.

Using Apple’s voice assistant Siri as an example, at an AI summit held by Apple in February last year, employees were informed that Siri would integrate a large language model in the future. However, former Siri engineer John Burkey revealed to The New York Times that Siri’s programming is quite complex, requiring six weeks to rebuild the database for each new sentence added.

On the other hand, amid Apple’s AI research facing challenges, interest in its Vision Pro headset device has also begun to wane, with recent sales cooling rapidly. As per a report by Mark Gurman from Bloomberg, he has indicated that demands for Vision Pro demos are way down at Apple stores, and sales of Vision Pro at some stores have dropped from a few units per day to a few units per week.

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(Photo credit: Apple)

Fueled by the rising demand for generative AI, global semiconductor testing equipment giant Advantest has revised upward its fiscal projections for the year spanning from April 2023 to March 2024. Advantest witnessed a significant surge in sales in the last quarter (October-December 2023), particularly in the Taiwan market.

In a press release issued on January 31, Advantest attributed the revision to the increasing demand for testing equipment driven by the surge in generative AI requirements. Consequently, the company raised its consolidated revenue target for the fiscal year from the initial forecast of JPY 470 billion to JPY 480 billion (a year-on-year decrease of 14.3%).

Similarly, the consolidated operating income target was adjusted upward from JPY 80 billion to JPY 85 billion (a year-on-year decrease of 49.3%), and the consolidated net income target was raised from JPY 60 billion to JPY 64.5 billion (a year-on-year decrease of 50.5%).

Advantest also released its financial results for the last quarter (October-December 2023) net sales decreased by 3.4% year-on-year to JPY 133.2 billion, consolidated operating income decreased significantly by 34.9% to JPY 26.8 billion, and consolidated net income decreased by 26.0% to JPY 21.2 billion.

Advantest noted that the demand for testing equipment for high-performance DRAM, such as HBM used in the generative AI sector, is on the rise. Therefore, the company has raised its sales forecast for memory testing equipment for the current fiscal year by JPY 5 billion to JPY 81 billion.

“In order to fulfill all the demand increase in the market we need to make further efforts,” stated by Advantest CEO Yoshiaki Yoshida stated during the financial results briefing held on January 31st.

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(Photo credit: Advantest’s Facebook)

In the dynamic wave of generative AI, AI PCs emerge as a focal point in the industry’s development. Technological upgrades across the industry chain and the distinctive features of on-device AI, such as security, low latency, and high reliability, drive their rapid evolution. AI PCs are poised to become a mainstream category within the PC market, converging with the PC replacement trend, reported by Jiwei.

On-Device AI, driven by technologies like lightweighting language large models (LLMs), signifies the next stage in AI development. PC makers aim to propel innovative upgrades in AI PC products by seamlessly integrating resources both upstream and downstream. The pivotal upgrade lies in the chip, with challenges in hardware-software coordination, data storage, and application development being inevitable. Nevertheless, AI PCs are on track to evolve at an unprecedented pace, transforming into a “hybrid” encompassing terminals, edge computing, and cloud technology.

Is AI PC Industry Savior?

In the face of consecutive quarters of global PC shipment decline, signs of a gradual easing in the downward trend are emerging. The industry cautiously anticipates a potential recovery, considering challenges such as structural demand cooling and supply imbalances.

Traditionally viewed as a mature industry grappling with long-term growth challenges, the PC industry is witnessing a shift due to the evolution of generative AI technology and the extension of the cloud to the edge. This combination of AI technology with terminal devices like PCs is seen as a trendsetter, with the ascent of AI PCs considered an “industry savior” that could open new avenues for growth in the PC market.

Yuanqing Yang, Chairman and CEO of Lenovo, elaborates on the stimulation of iterative computation and upgrades in AI-enabled terminals by AIGC. Recognizing the desire to enjoy the benefits of AIGC while safeguarding privacy, personal devices or home servers are deemed the safest. Lenovo is poised to invest approximately 7 billion RMB in the AI field over the next three years.

Analysis from Orient Securities, also known as DFZQ, reveals that the surge in consumer demand from the second half of 2020 to 2021 is expected to trigger a substantial PC replacement cycle from the second half of 2024 to 2025, initiating a new wave of PC upgrades.

Undoubtedly, AI PCs are set to usher in a transformative wave and accelerate development against the backdrop of the PC replacement trend. Guotai Junan Securities said that AI PCs feature processors with enhanced computing capabilities and incorporating multi-modal algorithms. This integration is anticipated to fundamentally reshape the PC experience, positioning AI PCs as a hybrid terminals, edge computing, and cloud technology to meet the new demands of generative AI workloads.

PC Ecosystem Players Strategically Positioning for Dominance

The AI PC field is experiencing vibrant development, with major PC ecosystem companies actively entering the scene. Companies such as Lenovo, Intel, Qualcomm, and Microsoft have introduced corresponding innovative initiatives. Lenovo showcased the industry’s first AI PC at the 2023 TechConnect World Innovation, Intel launched the AI PC Acceleration Program at its Innovation 2023, and Qualcomm introduced the Snapdragon X Elite processor specifically designed for AI at the Snapdragon Summit. Meanwhile, Microsoft is accelerating the optimization of office software, integrating Bing and ChatGPT into the Windows.

While current promotions of AI PC products may exceed actual user experiences, terminals displayed by Lenovo, Intel’s AI PC acceleration program, and the collaboration ecosystem deeply integrated with numerous independent software vendors (ISVs) indicate that the upgrade of on-device AI offers incomparable advantages compared to the cloud. This includes integrating the work habits of individual users, providing a personalized and differentiated user experience.

Ablikim Ablimiti, Vice President of Lenovo, highlighted five core features of AI PCs: possessing personal large models, natural language interaction, intelligent hybrid computing, open ecosystems, and ensuring real privacy and security. He stated that the encounter of AI large models with PCs is naturally harmonious, and terminal makers are leading this innovation by integrating upstream and downstream resources to provide a complete intelligent service for AI PCs.

In terms of chips, Intel Core Ultra is considered a significant processor architecture change in 40 years. It adopts the advanced Meteor Lake architecture, fully integrating chipset functions into the processor, incorporating NPU into the PC processor for the first time, and also integrating the dazzling series core graphics card. This signifies a significant milestone in the practical commercial application of AI PCs.

TrendForce: AI PC Demand to Expand from High-End Enterprises           

TrendForce believes that due to the high costs of upgrading both software and hardware associated with AI PCs, early development will be focused on high-end business users and content creators. This group has a strong demand for leveraging AI processing capabilities to improve productivity efficiency and can also benefit immediately from related applications, making them the primary users of the first generation. The emergence of AI PCs is not expected to necessarily stimulate additional PC purchase demand. Instead, most upgrades to AI PC devices will occur naturally as part of the business equipment replacement cycle projected for 2024.

(Image: Qualcomm)

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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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(Photo credit: Google)