AI Chip Design and Arm's Insights on Energy Efficiency, Trends & Challenges
18 April 2025 路 Uncategorized 路
Source: 路 https://technews.tw/2025/04/16/arm-talk-about-ai-design-thinking/
Arm, a semiconductor IP leader, recently released its trend report "New Thinking for Chips: Laying the Foundation of the AI Era," highlighting how rising demand for artificial intelligence (AI) is driving chip design toward high-performance computing to manage increasingly complex computational workloads. The era of AI prioritizes energy efficiency above all else.
The report identifies three key technological trends fueled by the rise of AI: advancements in high-performance computing, widespread adoption of custom silicon鈥攕uch as AWS Graviton4 and Azure Cobalt utilizing Arm architecture鈥攁nd the emergence of Computational Subsystems (CSS) and chiplet technology. CSS accelerates product development cycles, while chiplets enable customized chips with improved balance between power consumption, performance, and area.
Research from Goldman Sachs projects a 160% increase in data center electricity demand due to AI; therefore, optimizing configurations based on the specific characteristics of AI tasks will be essential for enhancing efficiency and reducing energy usage. Within AI computing, computation, data movement, and cooling are primary contributors to energy expenditure.
From an industry perspective, there is a growing focus on enhanced energy efficiency, leading to smaller, more efficient chips suitable for edge devices running generative AI applications like medical diagnostics or local voice recognition.
Arm also notes that AI innovation is driving advancements in chip architecture with technologies such as SVE2 and SME. The company anticipates the emergence of hybrid cloud-edge parallel architectures alongside continued growth in edge computing adoption.
Regarding cybersecurity, Arm鈥檚 Application Engineering Director Xu Dayong suggests future security measures will involve "AI vs AI," requiring System on Chip (SoC) designs to incorporate more robust encryption mechanisms.
Currently, a comprehensive restructuring is underway within chip design鈥攁cross architecture, collaboration, and standardization鈥攄riven by the demands of AI. Collaboration among IP suppliers, foundries, packaging plants, and system integrators will be critical; designs must shift toward holistic optimization considering computation, memory, cooling, power supply, and other factors. Furthermore, new standards supporting interfaces for smaller chips are needed.
Finally, Arm acknowledges challenges in software-chip integration, including high deployment costs on custom hardware, a lack of standardized AI development processes, and interoperability issues among frameworks.
The report identifies three key technological trends fueled by the rise of AI: advancements in high-performance computing, widespread adoption of custom silicon鈥攕uch as AWS Graviton4 and Azure Cobalt utilizing Arm architecture鈥攁nd the emergence of Computational Subsystems (CSS) and chiplet technology. CSS accelerates product development cycles, while chiplets enable customized chips with improved balance between power consumption, performance, and area.
Research from Goldman Sachs projects a 160% increase in data center electricity demand due to AI; therefore, optimizing configurations based on the specific characteristics of AI tasks will be essential for enhancing efficiency and reducing energy usage. Within AI computing, computation, data movement, and cooling are primary contributors to energy expenditure.
From an industry perspective, there is a growing focus on enhanced energy efficiency, leading to smaller, more efficient chips suitable for edge devices running generative AI applications like medical diagnostics or local voice recognition.
Arm also notes that AI innovation is driving advancements in chip architecture with technologies such as SVE2 and SME. The company anticipates the emergence of hybrid cloud-edge parallel architectures alongside continued growth in edge computing adoption.
Regarding cybersecurity, Arm鈥檚 Application Engineering Director Xu Dayong suggests future security measures will involve "AI vs AI," requiring System on Chip (SoC) designs to incorporate more robust encryption mechanisms.
Currently, a comprehensive restructuring is underway within chip design鈥攁cross architecture, collaboration, and standardization鈥攄riven by the demands of AI. Collaboration among IP suppliers, foundries, packaging plants, and system integrators will be critical; designs must shift toward holistic optimization considering computation, memory, cooling, power supply, and other factors. Furthermore, new standards supporting interfaces for smaller chips are needed.
Finally, Arm acknowledges challenges in software-chip integration, including high deployment costs on custom hardware, a lack of standardized AI development processes, and interoperability issues among frameworks.