From Reaction To Prevention In Data Center RAS

The rise of artificial intelligence (AI), cloud services, and IoT has fueled the rapid expansion of hyperscale data centers. These massive facilities house thousands of servers, all working to support an increasingly digital world. But as the scale of data centers grows, so too does the need for reliable and high-performance semiconductors. Semiconductor failures and inconsistencies can cause significant problems, especially when dealing with the real-time processing demands of AI and mission-critical applications. In such environments, the reliability, availability, and serviceability (RAS) of systems become paramount.

To address these challenges, proteanTecs introduces RTHM, real-time health monitoring, a cutting-edge application designed to predict and prevent failures before they happen. By shifting the focus from error detection to failure prediction, RTHM is set to redefine the future of data center reliability.

The challenges of semiconductor reliability in data centers

The semiconductor industry has made tremendous strides in performance, driven by the demands of AI, big data, and high-performance computing. Smaller process geometries and advanced chip architectures have enabled the development of faster, more energy-efficient chips. However, these advances come with challenges, especially considering the sheer volumes of chips in these data centers. Scale is defined by both quantity and connectivity, as architectures today rely on clusters with cross-system dependency. Defects, reliability issues, and yield concerns are magnified as semiconductor components shrink and become more complex. Some challenges in semiconductor reliability include:

• Smaller features: Advanced chips with smaller transistors are more prone to defects due to imperfections during manufacturing.
• Hot spots: Nanometer-scale transistors experience increased temperatures, which can lead to thermal management issues.
• Electromigration: High current densities in smaller components can cause the migration of metal atoms, leading to interconnect failures.
• Accelerated aging: Wear and tear from demanding applications can shorten the lifespan of semiconductors.

These challenges are further compounded by the intense operational conditions faced by modern data centers. High temperatures, mechanical stress, and near-threshold voltages put significant strain on semiconductors, making them more susceptible to failure over time.

Limitations of traditional RAS solutions

Traditional RAS strategies often employ a combination of software (SW) and hardware (HW) error monitoring to detect and correct failures in data centers.

Software (SW) monitoring, while scalable and flexible, has several key limitations. It detects errors only after they have propagated through the system, leading to high detection latency. This delayed response often makes it difficult to pinpoint the root cause, resulting in complex and resource-intensive recovery processes. SW monitoring also has blind spots, especially at the hardware level, where transient errors or low-voltage fluctuations can go unnoticed. Additionally, it lacks the ability to predict failures in advance, making it a reactive solution that focuses on error containment rather than prevention.

In contrast, HW monitoring offers real-time, low-latency detection of failures at the component level, allowing for faster and more accurate intervention. By embedding monitoring agents directly within the semiconductor, it can provide predictive insights into potential failures, enabling proactive maintenance and prescriptive actions before issues escalate.

Fig. 1: Software vs hardware error detection and mitigation.

Monitoring at the HW level also addresses critical challenges like Silent Data Corruption (SDC), which SW monitoring often misses. Overall, HW monitoring ensures higher reliability and cost-effective maintenance by preventing failures before they impact system performance.

Real-time health monitoring

proteanTecs’ Real-Time Health Monitoring (RTHM) application offers a paradigm shift in how RAS is managed. Instead of relying on error detection and mitigation after failures have occurred, RTHM uses deep in-chip monitoring and real-time algorithms to predict failures before they happen. By continuously tracking the health of semiconductors at the logic-path level, RTHM provides advanced warning of potential issues, allowing for proactive maintenance and failure prevention.

Fig. 2: Semiconduction failure rate; “Reliability bathtub curve,” before and after RTHM.

Key enables:

• Predictive maintenance: By continuously monitoring timing margins, RTHM can detect signs of degradation before they lead to failure.
• Prescriptive maintenance: When degradation is detected, RTHM provides prescriptive actions to mitigate the risk of failure.
• Fast imminent failure detection: In cases where degradation occurs too rapidly for proactive measures, RTHM provides fast failure detection, alerting the system to the issue before it propagates and pinpointing the exact source of the problem.

To learn more, download the RTHM white paper here.

How RTHM works: Monitoring timing margins

At the heart of RTHM is the continuous monitoring of timing margins in semiconductors. Timing margin refers to the amount of leeway a system has before it encounters a failure due to timing issues. Various factors, including aging, voltage fluctuations, and application workload, can cause timing margins to degrade over time.

Fig. 3: Design guard bands must be monitored during the operational lifetime of the system.

proteanTecs’ patented technology embeds “Margin Agents” within the chip, which measure timing margins in real-time without disrupting normal functionality. These agents provide highly accurate and actionable data on how close a device is to failure, allowing the system to take corrective actions before the failure occurs.

Using real-time algorithms, RTHM calculates a Performance Index (PI), which reflects the health of the semiconductor and the embedding system based on how low the timing margins have degraded, how widespread the issue is, and whether the degradation is permanent or transient. The PI allows system managers to assess the risk of failure and take appropriate action, such as adjusting operating conditions or scheduling repairs.

Fig. 4: Visualization of RTHM in a 5nm data center chip, operating in mission-mode (RTHM is a real-time FW application).

The benefits of proactive failure avoidance

By predicting failures before they occur, RTHM offers several key benefits to data centers:

1. Enhanced reliability: With the ability to predict and prevent failures, data centers can operate with higher reliability, which is critical for mission-critical applications like AI, healthcare, and financial services.
2. Lower costs: Preventing failures before they happen reduces the need for costly repairs and minimizes the financial impact of system outages.
3. Improved performance: RTHM ensures that data centers can maintain optimal performance levels, even as workloads and demands increase.
4. Reduced downtime: Proactive failure avoidance means that systems can remain operational without interruption, even as components age or degrade.

Conclusion: A new era of data center reliability

As data centers continue to scale and support increasingly complex and demanding applications, the need for reliable semiconductors has never been greater. Traditional RAS solutions, while effective, fall short in addressing the challenges of modern data centers, particularly in the face of emerging threats like Silent Data Corruption.

proteanTecs’ Real-Time Health Monitoring (RTHM) represents a fundamental shift in how data center reliability is managed. By predicting failures before they happen and providing prescriptive maintenance solutions, RTHM enables data centers to operate with unprecedented reliability and efficiency. As AI and other cutting-edge technologies continue to evolve, RTHM will play a critical role in ensuring that data centers can keep up with the demands of the future.

Read the white paper here.