Scaling GenAI Training and Inference Chips With Runtime Monitoring

Abstract
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GenAI’s rapid growth is pushing the limits of semiconductor technology, demanding breakthroughs in performance, power efficiency, and reliability. Training and inference workloads for models like GPT-4 and GPT-5 require massive computational resources, leading to skyrocketing costs, energy consumption, and hardware failures. Traditional optimization methods, such as static guard bands and periodic testing, fail to address the dynamic and workload-specific challenges posed by GenAI.

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This white paper features proteanTecs dedicated suite of embedded solutions purpose-built for AI workloads, offering applications engineered to dynamically reduce power, prevent failures and optimize throughput.

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You'll Learn:

• The critical challenges of scaling GenAI compute
• A new approach for real-time monitoring of chip performance, power, and reliability
• Ways to reduce power consumption by up to 14%  
• Techniques to boost chip performance by up to 10%  
• Ways to prevent functional failures and SDC in AI clusters
• Strategies for workload balancing and fleet-level reliability improvements.