Software challenges may pose a greater obstacle than hardware for semiconductor firms
In the AI landscape, China's homegrown hardware and software ecosystems have been under development since 2015. This push began with the emergence of the four "AI dragons" in China, including companies like Huawei, Alibaba, Baidu, and traditional chip manufacturers.
Fast forward to early 2023, and the Chinese government has taken a more active role in supporting AI hardware development, particularly through companies like Huawei. The tech giant unveiled the Cloudmatrix chip cluster, a formidable challenge to Nvidia's dominance, connecting 384 Ascend AI chips. The government also promoted a domestic semiconductor industry, mandating that state cloud providers source at least 50% of their chips from local manufacturers, including startups like Deepseek using Huawei chips to train AI models.
However, the rise of large models in AI technology has given Chinese AI chip manufacturers a rare opportunity to catch up. This opportunity came amidst Nvidia's prohibition on supplying high-end GPU chips to the Chinese market in 2022, as well as US chip bans and a global shortage of computing power.
In response, Chinese AI chip manufacturers like Biren, Cambricon, Houmo.ai, Moore Threads, and Iluvatar CoreX have developed their own AI software platforms. However, the absence of CUDA-based software compatibility has presented a significant challenge.
If AI chip manufacturers can provide easy-to-use and low-cost migration tools, and offer comprehensive model adaptation capabilities, rapid implementation of large models becomes feasible. To achieve this, Chinese AI companies are working on localizing the large model software, computing power, and cloud computing layers to maximize overall performance.
Biren, for instance, has partnered with framework developers like PaddlePaddle and computing power optimization players like Infinigence to promote the localization of AI computing efficiently. This strategic collaboration is aimed at building computing power platforms, open-sourcing related tools and libraries, and establishing ecosystem collaborations.
The software for these chips includes firmware, drivers, operating systems, applications, and development tools. The semiconductor industry is highly integrated with both hardware and software, a fact that has become increasingly apparent as large language models (LLMs) have become the frontier technology pursued worldwide.
The first-mover advantage of CUDA, which made Nvidia a dominant force in the AI era, has narrowed due to this rapid convergence in the AI industry. As a result, companies like Biren are promoting their software platforms by building computing power platforms, open-sourcing related tools and libraries, and opening upper-level models to establish ecosystem collaborations.
The urgent demand for computing clusters in large model training has led Chinese AI chip companies to strengthen their cluster capabilities. This push is evident in the development of Biren's SUPA, which has shown promising results in mainstream open-source large models with software team support.
The journey of China's AI chip companies began in 2012, when deep learning and GPU made a significant impact at the ImageNet competition, leading to global interest in artificial intelligence. Since then, the landscape has evolved, with the Transformer network becoming the frontier technology pursued worldwide. Initially having three different paths (BERT, T5, and GPT), the Transformer network saw GPT become the absolute mainstream due to the stunning debut of ChatGPT.
In conclusion, China's AI chip companies are racing to catch up in the AI era, facing challenges due to global politics and technological barriers. However, with strategic collaborations, localization efforts, and a focus on providing easy-to-use and efficient solutions, these companies are poised to make significant strides in the global AI market.
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