According to a report by Taiwanese media Money DJ, after establishing a stable position as a major supplier for NVIDIA GPU baseboard, Wistron has secured orders for AMD MI300 baseboards. Reliable sources indicate that Wistron has expanded its involvement beyond AMD baseboards and entered the module assembling segment.

In addition to NVIDIA and AMD, Wistron has also entered the Intel AI chip module and baseboard supply chain, encompassing orders from the three major AI chip manufacturers.

The NVIDIA AI server supply chain includes GPU modules, GPU baseboards, motherboards, server systems, complete server cabinets, and more. Wistron holds a significant share in GPU baseboard supply and is also involved in server system assembly.

Currently, NVIDIA commands a 70% market share in AI chips, but various chip manufacturers are eager to compete. Both AMD and Intel have introduced corresponding solutions. While Wistron was previously rumored to have entered AMD baseboard supply, it has also ventured into AMD GPU module assembling, serving as the sole source, according to reliable sources.

Regarding the news of Wistron’s involvement in AMD and Intel chip manufacturing, the company has chosen not to respond to market rumors.

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In the realm of specifications competition, desktop computers continue to possess numerous irreplaceable advantages. These include ease of upgrading, superior heat dissipation capabilities, and robust and durable construction, resulting in extended usage lifespans. As a result, desktop computers maintain a steadfast market demand. Due to the ease of component replacement in desktops, expandability remains a significant advantage for PC gamers. For creators and business professionals, desktop computers satisfy extensive external connectivity needs while offering superior heat dissipation. Furthermore, owing to the size limitations of laptops, desktop computers continue to provide a more comfortable user experience during prolonged usage.

Windows 10 Exit and Hardware Updates Set to Drive 2024 Upgrade Trend

In the latter half of 2022, brands and retailers aggressively cleared their inventories, a trend that continued into 2023, resulting in a sustained challenging period for the PC market. In recent years, the PC market has approached saturation, making it difficult to drive market growth through sheer quantity. Consequently, brand manufacturers have focused on business, gaming, and creator products. However, PCs inherently belong to a cyclical terminal market. With the Windows 10 operating system set to retire in October 2025 and Windows 11’s heightened hardware specifications requirements, products released before 2017 will require replacements. Additionally, it is anticipated that companies like Intel, AMD, and NVIDIA will gradually unveil new products in the latter half of 2023. This, coupled with the demands of the new operating system, is expected to trigger a noticeable upgrade trend among consumers, ultimately providing a glimmer of hope for the PC market. (Image credit: Unsplash_Alienwaregaming)

As applications like AIGC, 8K, AR/MR, and others continue to develop, 3D IC stacking and heterogeneous integration of chiplet have become the primary solutions to meet future high-performance computing demands and extend Moore’s Law.

Major companies like TSMC and Intel have been expanding their investments in heterogeneous integration manufacturing and related research and development in recent years. Additionally, leading EDA company Cadence has taken the industry lead by introducing the “Integrity 3D-IC” platform, an integrated solution for design planning, realization, and system analysis simulation tools, marking a significant step towards 3D chip stacking.

Differences between 2.5D and 3D Packaging

The main difference between 2.5D and 3D packaging technologies lies in the stacking method. 2.5D packaging involves stacking chips one by one on an interposer or connecting them through silicon bridges, primarily used for assembling logic processing chips and high-bandwidth memory. On the other hand, 3D packaging is a technology that vertically stacks chips, mainly targeting high-performance logic chips and SoC manufacturing.

CPU and HBM Stacking Demands

With the rapid development of applications like AIGC, AR/VR, and 8K, it is expected that a significant amount of computational demand will arise, particularly driving the need for parallel computing systems capable of processing big data in a short time. To overcome the bandwidth limitations of DDR SDRAM and further enhance parallel computing performance, the industry has been increasingly adopting High-Bandwidth Memory (HBM). This trend has led to a shift from the traditional “CPU + memory (such as DDR4)” architecture to the “Chip + HBM stacking” 2.5D architecture. With continuous growth in computational demand, the future may see the integration of CPU, GPU, or SoC through 3D stacking.

3D Stacking with HBM Prevails, but CPU Stacking Lags Behind

HBM was introduced in 2013 as a 3D stacked architecture for high-performance SDRAM. Over time, the stacking of multiple layers of HBM has become widespread in packaging, while the stacking of CPUs/GPUs has not seen significant progress.

The main reasons for this disparity can be attributed to three factors: 1. Thermal conduction, 2. Thermal stress, and 3. IC design. First, 3D stacking has historically performed poorly in terms of thermal conduction, which is why it is primarily used in memory stacking, as memory operations generate much less heat than logic operations. As a result, the thermal conduction issues faced by current memory stacking products can be largely disregarded.

Second, thermal stress issues arise from the mismatch in coefficients of thermal expansion (CTE) between materials and the introduction of stress from thinning the chips and introducing metal layers. The complex stress distribution in stacked structures has a significant negative impact on product reliability.

Finally, IC design challenges from a lack of EDA tools, as traditional CAD tools are inadequate for handling 3D design rules. Developers must create their own tools to address process requirements, and the complex design of 3D packaging further increases the design, manufacturing, and testing costs.

How EDA Companies Offer Solutions

Cadence, during the LIVE Taiwan 2023 user annual conference, highlighted its years of effort in developing solutions. They have introduced tools like the Clarity 3D solver, Celsius thermal solver, and Sigrity Signal and Power Integrity, which can address thermal conduction and thermal stress simulation issues. When combined with Cadence’s comprehensive EDA tools, these offerings contribute to the growth of the “Integrity 3D-IC” platform, aiding in the development of 3D IC design.

“3D IC” represents a critical design trend in semiconductor development. However, it presents greater challenges and complexity than other projects. In addition to the challenges in Logic IC design, there is a need for analog and multi-physics simulations. Therefore, cross-platform design tools are indispensable. The tools provided by EDA leader Cadence are expected to strengthen the 3D IC design tool platform, reducing the technological barriers for stacking CPU, GPU, or SoC to enhance chip computing performance.

This article is from TechNews, a collaborative media partner of TrendForce.

(Photo credit: TSMC)

According to Taiwan’s TechNews report, Lu Donghui, Chairman of Micron Technology Taiwan, stated that in response to the growing demand in the AI market, Micron Technology Taiwan will continue to invest in advanced processes and packaging technologies to produce High Bandwidth Memory (HBM) products. Micron Technology Taiwan is the only Micron facility globally with advanced packaging capabilities.

Lu Donghui, speaking at a media event, mentioned that Micron had previously introduced the industry’s first 8-layer stack (8-High) 24GB HBM3 Gen 2 product, which is now in the sampling phase. This product boasts a bandwidth exceeding 1.2TB/s and a transmission rate exceeding 9.2Gb/s, which is 50% higher than other HBM3 solutions on the market. Micron’s HBM3 Gen 2 product offers 2.5 times better energy efficiency per watt compared to previous generations, making it ideal for high-performance AI applications.

Micron’s HBM3 Gen 2 memory products are manufactured using the most advanced 1-beta process technology in Taiwan and Japan. Compared to the previous 1-alpha process, the 1-beta process reduces power consumption by approximately 15% and increases bit density by over 35%, with each chip offering a capacity of up to 16Gb. Through Micron’s advanced packaging technology, the 1-beta process memory chips are stacked in 8 layers, and the complete HBM3 Gen 2 chips are packaged and sent to customers’ specified semiconductor foundries like TSMC, Intel, Samsung, or third-party packaging and testing facilities for GPUs, CPUs.

Lu Donghui highlighted that Taiwan’s robust semiconductor manufacturing ecosystem makes it the exclusive hub for Micron’s advanced packaging development worldwide. By combining this ecosystem with Micron’s offerings, they can provide customers with comprehensive solutions to meet market demands. While HBM products represent a relatively small portion of the overall memory market, their future growth potential is significant, with expectations to capture around 10% of the entire memory market in the short term.

(Photo credit: Micron)

According to the news from Chinatimes, Asus, a prominent technology company, has announced on the 30th of this month the release of AI servers equipped with NVIDIA’s L40S GPUs. These servers are now available for order. The L40S GPU was introduced by NVIDIA in August to address the shortage of H100 and A100 GPUs. Remarkably, Asus has swiftly responded to this situation by unveiling AI server products within a span of less than two weeks, showcasing their optimism in the imminent surge of AI applications and their eagerness to seize the opportunity.

Solid AI Capabilities of Asus Group

Apart from being among the first manufacturers to introduce the NVIDIA OVX server system, Asus has leveraged resources from its subsidiaries, such as TaiSmart and Asus Cloud, to establish a formidable AI infrastructure. This not only involves in-house innovation like the Large Language Model (LLM) technology but also extends to providing AI computing power and enterprise-level generative AI applications. These strengths position Asus as one of the few all-encompassing providers of generative AI solutions.

Projected Surge in Server Business

Regarding server business performance, Asus envisions a yearly compounded growth rate of at least 40% until 2027, with a goal of achieving a fivefold growth over five years. In particular, the data center server business catering primarily to Cloud Service Providers (CSPs) anticipates a tenfold growth within the same timeframe, driven by the adoption of AI server products.

Asus CEO recently emphasized that Asus’s foray into AI server development was prompt and involved collaboration with NVIDIA from the outset. While the product lineup might be more streamlined compared to other OEM/ODM manufacturers, Asus had secured numerous GPU orders ahead of the AI server demand surge. The company is optimistic about the shipping momentum and order visibility for the new generation of AI servers in the latter half of the year.

Embracing NVIDIA’s Versatile L40S GPU

The NVIDIA L40S GPU, built on the Ada Lovelace architecture, stands out as one of the most powerful general-purpose GPUs in data centers. It offers groundbreaking multi-workload computations for large language model inference, training, graphics, and image processing. Not only does it facilitate rapid hardware solution deployment, but it also holds significance due to the current scarcity of higher-tier H100 and A100 GPUs, which have reached allocation stages. Consequently, businesses seeking to repurpose idle data centers are anticipated to shift their focus toward AI servers featuring the L40S GPU.

Asus’s newly introduced L40S GPU servers include the ESC8000-E11/ESC4000-E11 models with built-in Intel Xeon processors, as well as the ESC8000A-E12/ESC4000A-E12 models utilizing AMD EPYC processors. These servers can be configured with up to 4 or a maximum of 8 NVIDIA L40S GPUs. This configuration assists enterprises in enhancing training, fine-tuning, and inference workloads, facilitating AI model creation. It also establishes Asus’s platforms as the preferred choice for multi-modal generative AI applications.

(Source: https://www.chinatimes.com/newspapers/20230831000158-260202?chdtv)