【高通Qualcomm】2025AI变革正在推动终端侧推理创新研究报告_12页_883kb

AI Disruption and On-Device Inference Innovation

AI models are evolving to be smaller and more efficient, enabling on-device inference. Key advancements include distilled models that match or outperform larger alternatives, reducing development costs and deployment time while maintaining high performance. This drives demand for powerful edge computing chips, with Qualcomm positioned as a leader due to its extensive hardware expertise and software ecosystem.

Key Trends

• Smaller AI Models: Techniques like model distillation and quantization improve model quality and reduce size, making AI accessible for real-world applications at the edge.
• Edge Deployment Benefits: On-device inference offers reduced latency, enhanced privacy, and lower costs, accelerating AI adoption across various devices.
• Qualcomm's Role: Strategically positioned in AI inference through Snapdragon platforms, providing integrated hardware for smartphones, PCs, automotive, industrial IoT, and networking. Collaborative tools like the Qualcomm AI Hub support developers in AI innovation.
• Industry Applications: AI is transforming sectors such as mobile enhancement, automotive safety, industrial automation, and networking by optimizing processes for edge devices.

Conclusion

The AI landscape is shifting toward edge inference, with Qualcomm at the forefront, promoting scalable and efficient AI deployment for broader commercial impact.