How China’s Semiconductor Industry Is Forging Its Own Path Amidst Technological Restrictions

The Resilience of China’s Chip Sector in the Face of Export Controls

Since 2018, the United States has implemented increasingly stringent export restrictions on semiconductor technology to China, aiming to curb the nation’s advancement in high-tech industries. However, rather than stifling innovation, these limitations have catalyzed a unique approach to technological development within China’s chipmaking sector. Companies are now pushing existing tools to their absolute limits, scaling up processing power through massive clusters, and employing novel mathematical approaches to maximize performance from constrained hardware.

The Resilience of China’s Chip Sector in the Face of Export Controls
Performance Gaps and Manufacturing Challenges
Compensating Through Scale and System Architecture
Hardware-Software Co-design and Computational Efficiency
The Road to Technological Self-Sufficiency

Performance Gaps and Manufacturing Challenges

Current analysis reveals significant performance disparities between Chinese and American chips. The median performance of Chinese processors sits at approximately 114 teraflops, substantially behind their Western counterparts. Huawei’s flagship Ascend 910 chip delivers 800 teraflops compared to Nvidia’s H200 at 2,500 teraflops. This gap primarily stems from manufacturing limitations, as only three companies worldwide—Samsung Semiconductor, TSMC, and Intel—possess the capability to produce chips with the most advanced transistor technology.

The heart of the manufacturing challenge lies in lithography equipment. Advanced extreme-ultraviolet (EUV) lithography machines, essential for creating the most sophisticated chips, remain inaccessible to Chinese firms due to export restrictions. This has forced manufacturers to innovate with older deep-ultraviolet (DUV) systems through techniques like multi-patterning, which involves repeatedly exposing silicon wafers to build up finer circuit details. While this approach enables some advancement, it comes with significant trade-offs in production cost, speed, and yield rates., according to industry news

Compensating Through Scale and System Architecture

Facing quality limitations, Chinese companies are pursuing quantity and architectural innovation. Huawei’s recently announced CloudMatrix 384 system exemplifies this strategy, connecting 384 Ascend 910 chips to compete with Nvidia’s systems containing far fewer but more powerful processors. While this approach requires approximately five times as many chips to achieve less than double the performance of competing systems, it represents a pragmatic solution given current constraints.

The power consumption implications are substantial—Huawei’s system consumes over 600kW, more than quadruple that of comparable Nvidia installations. However, industry analysts note that energy availability in China makes this trade-off acceptable. More importantly, this approach leverages Huawei’s historical strengths in networking technology, incorporating optical data transfer methods that reduce power consumption and heat generation compared to traditional electrical connections.

Hardware-Software Co-design and Computational Efficiency

Perhaps the most innovative aspect of China’s approach involves tightly integrating hardware with specialized software. Recognizing that AI models can tolerate mathematical imprecision, Chinese firms are developing chips that use reduced numerical precision—representing numbers with just 8, 4, or even fewer bits compared to the 32 or 64 bits standard in general-purpose processors., according to industry analysis

DeepSeek, emerging as a standard-setter in China’s AI ecosystem, recently introduced a novel 8-bit number format that eliminates distinctions between positive and negative numbers and removes fractional components entirely. While this sacrifices numerical range and precision, it dramatically improves computational efficiency. Chip designers like Cambricon Technologies have quickly adopted support for this format, with Huawei expected to follow suit., according to market developments

The Road to Technological Self-Sufficiency

China’s progress toward a complete, domestic technology stack shows promise but faces significant hurdles. Nvidia’s CUDA programming platform remains dominant in AI development, and Chinese chip designers still rely on American electronic design automation tools from companies like Synopsys and Cadence, despite temporary export restrictions earlier this year.

Additionally, while Chinese chips are narrowing the gap in AI inference tasks, they trail in the training phase that creates AI models initially. Training requires sophisticated memory chips and the ability to rapidly move massive datasets—another area where export controls have limited Chinese access to advanced technology.

Nevertheless, China’s determination to build a self-reliant semiconductor industry appears unwavering. Recent developments, including government encouragement for firms to abandon Nvidia’s deliberately limited China-specific products in favor of domestic alternatives, signal a long-term commitment to technological independence. The strategy acknowledges that being competitive, rather than necessarily superior, may suffice for national security and economic needs., as previous analysis

The emerging Chinese approach—maximizing existing tools, scaling through quantity, and innovating in hardware-software integration—represents a distinct path in semiconductor development that could eventually challenge Western technological dominance, even if it currently trails in raw performance metrics.