芯：端侧需求升级推动算力平台革命

Summary of Conference Call on AI Computing Power Deployment Industry Overview - The conference focused on the deployment strategies of AI computing power, specifically comparing cloud-based and edge-based (device-side) processing methods [2][3]. Key Points and Arguments 1. Shift to Edge AI: The edge AI is expected to become mainstream due to its advantages in processing costs, availability, privacy security, performance, and personalized customization [2][4]. 2. Technological Advancements: Major brands like Intel, Qualcomm, and Apple are launching new products to enhance AI processing capabilities on devices, indicating a significant shift in the market [2][5]. 3. Importance of MPU: The MPU (Micro Processing Unit) is becoming essential in the AI era due to its high efficiency and low power consumption, particularly for high-load AI applications [6][11]. 4. Collaboration of Processing Units: NPU (Neural Processing Unit), CPU, and GPU are working together, each handling different computational tasks to improve efficiency and battery life [6][12]. 5. Rise of Arm Architecture: The Arm architecture is gaining traction in the PC market, supported by companies like Microsoft and Apple, which is expected to lead to a revolution in PC hardware and performance [8][13]. Additional Important Insights 1. Hybrid Use of Edge and Cloud: A combination of edge and cloud processing is anticipated to create a powerful technological synergy, promoting further development and application of AI technologies [4][9]. 2. NPU as a Competitive Segment: The NPU is becoming a standalone competitive segment due to its efficiency in handling high-load AI applications, which is crucial for extending device battery life [11][12]. 3. Market Demand for Arm Architecture: The increasing demand for high-performance, low-power Arm architecture chips is driven by the need for devices that can run AI loads for extended periods [13]. This summary encapsulates the critical discussions and insights from the conference call regarding the future of AI computing power deployment and the evolving landscape of the technology industry.