专访中昊芯英CTO郑瀚寻：国产AI芯片也将兼容不同平台

Core Insights - The demand for AI computing is driving attention towards AI chips beyond GPUs, with companies like Google and Groq leading the way in alternative technologies [1][3] - In the domestic market, ASIC custom chip manufacturers are rapidly developing, as the cost of specialized chips decreases, allowing more firms to explore personalized AI capabilities [2][4] AI Chip Market Trends - The trend of seeking development opportunities outside of GPU chips is becoming more pronounced, with companies recognizing that innovation is necessary to compete with NVIDIA [3][4] - The success of GPUs is largely attributed to NVIDIA's established engineering teams, which are not easily replicable by newcomers [3] Technological Advancements - The introduction of Tensor Cores in NVIDIA's Tesla V100 series has highlighted the efficiency of tensor processing units (TPUs) in handling large data volumes, offering significant computational advantages [4][5] - The scaling laws in AI models continue to demand higher performance from underlying AI computing clusters, presenting challenges for domestic XPU chips [5] Interconnectivity and Infrastructure - Companies are focusing on enhancing interconnectivity between chips, cabinets, and data centers to meet the demands of high-speed data transmission [5][6] - 中昊芯英 is exploring advanced interconnect technologies, such as OCS all-optical interconnects, to improve its capabilities [6] Competitive Landscape - NVIDIA's InfiniBand protocol is seen as a competitive advantage for large-scale data center deployments, while domestic firms are leaning towards Ethernet protocols for their flexibility and improved performance [6] - The development of software ecosystems is crucial for domestic AI chip platforms, as they need to build their own software stacks to compete with NVIDIA's established CUDA ecosystem [6][7] Future Directions - The evolution of AI models, particularly those based on the Transformer architecture, continues to shape the landscape, with ongoing optimizations and adaptations [7] - The compatibility and smooth operation of various platforms will be essential for the success of domestic AI chips, similar to the early days of the Android ecosystem [7]