#Huawei

At the 2026 International Symposium on Circuits and Systems in Shanghai, Huawei introduced 'Tao's Law,' which replaces geometric scaling with time scaling for semiconductor advancement. The company claims to have already designed and mass-produced 381 chips under this principle and plans to launch a new Kirin phone chip using logic folding this autumn. This could represent a fundamental shift in semiconductor scaling beyond Moore's Law, potentially extending chip performance improvements without relying solely on shrinking transistor sizes. If validated, it may impact the entire industry's R&D direction and reduce dependence on extreme lithography. According to Huawei, Tao's Law achieves multi-level co-optimization from devices to systems by reducing time constants instead of geometric dimensions. The company projects that by 2031, high-end chips based on this law could reach transistor density equivalent to 1.4nm process technology.

Geekerwan's review reveals that the Huawei Mate 80 Pro series, equipped with the Kirin 9030 chip, achieves superior gaming energy efficiency compared to the Snapdragon 8 Gen3, thanks to HarmonyOS native optimization and software-hardware synergy. This demonstrates Huawei's significant progress in chip design and software optimization, potentially reshaping mobile performance standards. It highlights how software-hardware integration can overcome theoretical hardware limitations, benefiting both consumers and the broader ecosystem. The Kirin 9030 Pro features a 9-core CPU and a 6-core Maleoon 935 GPU; in Genshin Impact at 60fps max settings, the Mate 80 Pro Max consumes only 4.9W, outperforming Snapdragon 8 Gen3 efficiency. The chip's transistor count is approximately 15 billion, with CPU multi-core efficiency between Snapdragon 8 Gen2 and Gen3.

Huawei has open-sourced OpenPangu-2.0-Flash, a Mixture-of-Experts (MoE) large language model with 92 billion total parameters and 6 billion active parameters, supporting a 512K token context length. The release includes model weights, inference code, and training operations. This open-sourcing provides the community with a high-performance, long-context MoE model from a major tech company, potentially lowering the barrier for researchers and developers to experiment with large-scale MoE architectures. It also signals Huawei's growing involvement in the open-source AI ecosystem. The model uses an MoE architecture where only 6B of the 92B parameters are activated per token, enabling efficient inference. It achieves a 512K token context window, and a larger Pro variant (505B total, 18B active) is slated for release in July.