Core Insights - The report by Arm highlights the increasing complexity in chip design due to the slowing of Moore's Law and the rising demands of AI workloads, emphasizing energy efficiency as a primary consideration for AI computing [1][2] - The semiconductor industry is shifting towards innovative alternatives such as custom chips, compute subsystems (CSS), and chiplets to enhance performance and efficiency [1][2] Chip Design Challenges - Achieving a balance between computing power and energy efficiency is a significant challenge, necessitating close collaboration with foundries to optimize transistors for both dynamic and leakage power [2] - Optimization must occur at various levels, from transistor to architecture, including SoC design and data center operations, with a focus on protecting data during transmission to reduce power consumption [2] Custom Chip Trends - Custom chips are becoming a crucial trend in the semiconductor industry, with major cloud service providers accounting for nearly half of global cloud server procurement spending in 2024 [3] - The key to custom chip design lies in ensuring high reusability between chips and software, which helps address cost and time-to-market challenges [3] Security Considerations - As AI technology evolves, so do security threats, prompting the semiconductor industry to develop multi-layered hardware and software protection systems [3] - AI is also being leveraged to enhance security measures, enabling rapid identification of suspicious activities and potential vulnerabilities [3] Software Ecosystem Importance - The software ecosystem is vital for unlocking the potential of new chip architectures, requiring seamless compatibility with AI frameworks and optimization support for custom chips [4] - The slowing of Moore's Law necessitates closer collaboration between chip design and manufacturing, with advanced packaging technologies driving innovation [4] Chiplet Technology - Chiplet technology is still in its early exploratory phase, with standardization of design and interfaces being critical for effective integration and communication between chiplets [5] - Arm's Chiplet System Architecture (CSA) aims to standardize communication methods among chiplets, ensuring interoperability across different suppliers [5] Advanced Packaging and Performance - The demand for AI computing is accelerating the convergence of various technologies, with chiplet designs allowing for modular isolation of different functional blocks to enhance cost-effectiveness [6] - Advanced packaging techniques, such as 3D packaging, improve performance and efficiency by reducing data transmission distances and power consumption [6] Standardization Benefits - The true value of advanced packaging and chiplet technology lies in standardization, enabling rapid configuration of chiplets to meet diverse performance needs, thus shortening product time-to-market [7]