手机芯片，需要这些创新

Core Viewpoint - The article discusses the evolution of smartphones into intelligent, context-aware devices through the integration of agentic AI, which enhances their capabilities beyond simple communication tools [1][3]. Hardware Challenges - The main challenge in implementing agentic AI on smartphones lies in hardware limitations, including battery life, processing power, and memory constraints [3]. - To meet the demands of agentic AI, significant upgrades in smartphone hardware components such as processors, memory, storage, battery, sensors, and thermal management are necessary [3]. Memory System Requirements - There is a growing demand for advanced memory subsystems to efficiently deliver data for AI applications on devices [5]. - The current standard LPDDR5X offers speeds up to approximately 10.7 Gbps, while the upcoming LPDDR6 standard promises faster bandwidth (14.4 Gbps+) and improved power efficiency [5]. Key Technologies for Memory Solutions 1. Memory Processing (PIM) Architecture: PIM integrates computing functions directly into memory, significantly reducing latency and power consumption, although standardization is still developing [7]. 2. Wide I/O Interfaces and Advanced Packaging: These methods enhance bandwidth and assist in thermal management, potentially allowing OEMs to offload DRAM to separate packages for AI-intensive workloads [11]. Importance of Quantization and Collaboration - Techniques like quantization are crucial for introducing GenAI into smartphones by reducing memory and computational demands while maintaining accuracy [15]. - Collaboration among SoC designers, memory and storage suppliers, OEMs, OS developers, and AI researchers is essential to optimize hardware and software for edge AI [16]. Industry Call to Action - The article emphasizes the need for a collective vision and investment in next-generation memory, storage, and packaging technologies to keep pace with rapid AI advancements and unlock the transformative potential of agentic AI in smartphones [17].