China Continues to Enhance AI Chip Self-Sufficiency, but High-End AI Chip Development Remains Constrained, Says TrendForce

Huawei’s subsidiary HiSilicon has made significant strides in the independent R&D of AI chips, launching the next-gen Ascend 910B. These chips are utilized not only in Huawei’s public cloud infrastructure but also sold to other Chinese companies. This year, Baidu ordered over a thousand Ascend 910B chips from Huawei to build approximately 200 AI servers. Additionally, in August, Chinese company iFlytek, in partnership with Huawei, released the “Gemini Star Program,” a hardware and software integrated device for exclusive enterprise LLMs, equipped with the Ascend 910B AI acceleration chip, according to TrendForce’s research.

TrendForce conjectures that the next-generation Ascend 910B chip is likely manufactured using SMIC’s N+2 process. However, the production faces two potential risks. Firstly, as Huawei recently focused on expanding its smartphone business, the N+2 process capacity at SMIC is almost entirely allocated to Huawei’s smartphone products, potentially limiting future capacity for AI chips. Secondly, SMIC remains on the Entity List, possibly restricting access to advanced process equipment.

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Market analysis indicates that the Ascend 910B’s performance slightly lags behind the A800 series and its software ecosystem significantly differs from NVIDIA’s CUDA, impacting usage efficiency. However, considering the potential expansion of U.S. restrictions, Chinese manufacturers might be compelled to shift towards the Ascend 910B. There remains considerable potential for China to improve and establish a complete AI ecosystem.

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US sanctions drive Chinese CSPs to increase investment in AI chip autonomy

Chinese CSPs like Baidu and Alibaba are actively investing in autonomous AI chip development. Baidu developed its first self-researched ASIC AI chip, Kunlunxin, in early 2020, with its second generation scheduled for mass production in 2021 and the third expected to launch in 2024. Post-2023, Baidu aims to use Huawei’s Ascend 910B acceleration chips and expand the use of Kunlunxin chips for its AI infrastructure.

After Alibaba’s acquisition of CPU IP supplier Zhongtian Micro Systems in April 2018 and the establishment of T-Head Semiconductor in September of the same year, the company began developing its own ASIC AI chips, including the Hanguang 800. TrendForce reports that T-Head’s initial ASIC chips were co-designed with external companies like GUC. However, after 2023, Alibaba is expected to increasingly leverage its internal resources to enhance the independent design capabilities of its next-gen ASIC chips, primarily for Alibaba Cloud’s AI infrastructure.

China’s high-end AI chip development is limited by the US Entity List and restrictions on advanced process EDA

The U.S. sanctions encompass both software and hardware aspects of China’s high-performance computing (HPC) and AI application sectors. Notably, in October 2023, the U.S. Department of Commerce added companies like Biren and Moore Threads to the Entity List. Additionally, regulations governing advanced manufacturing processes, such as logic ICs with processes finer than 16nm, DRAM with processes finer than 18nm, and NAND Flash with more than 128 layers designated for export to China, were introduced. These measures have extended the review criteria for AI chip hardware design beyond total processing performance to include performance density requirements, thereby complicating the supply of high-end AI chips from leading manufacturers like NVIDIA and AMD.

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Beyond the 2023 U.S. sanctions, the latter half of 2022 saw significant restrictions on EDA semiconductor design software tools, particularly affecting the design of advanced processes like Samsung’s 3nm or TSMC’s 2nm technologies. Although the mainstream market chips, such as NVIDIA’s A100 and AMD’s MI200, are based on the 6/7nm process, and upcoming models like NVIDIA H100 and AMD MI300 series are expected to shift to 4/5nm processes by 2024, TrendForce forecasts that, despite EDA restrictions not having an immediate significant impact in the short term, they will pose long-term challenges for China in adopting more advanced processes and in the development of next-gen, higher-performance HPC or AI chips.

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