A paradigm shift in semiconductor reliability, moving beyond error detection to failure avoidance.
Abstract
Hyperscale datacenters require intense computational power for compute-intensive tasks, such as AI, data analytics, machine learning, and big data processing. They leverage parallel processing across multiple computers, in high-density servers, to handle complex tasks efficiently. This uses specialized, powerful processors and training and inference of specific GPUs or ASICs. Such chips are based on the most cutting-edge semiconductor technology and smallest process geometries to achieve their goals. But while smaller process geometries and advanced architectures enable faster, more power-efficient chips, they also introduce challenges related to lifetime performance and reliability. In particular, the rise of silent data corruption (SDC), which can go undetected by conventional monitoring methods, threatens the integrity of data and AI model accuracy, leading to significant disruptions and financial losses.
The Real-Time Health Monitoring (RTHM) application is a proactive solution designed to predict and prevent failures before they occur. RTHM represents a paradigm shift in semiconductor reliability, moving beyond error detection to failure avoidance. By leveraging in-chip performance monitoring and real-time algorithms, RTHM enables predictive maintenance, prescriptive actions, and fast imminent failure detection. This paper explores the unique challenges posed by advanced electronics and demonstrates how RTHM can enhance reliability, availability, and serviceability (RAS) in high-performance data centers, making them resilient to the demands of modern cloud computing, AI, and high-performance workloads, while minimizing the risk of costly system failures.
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