edfas.org 25 ELECTRONIC DEVICE FAILURE ANALYSIS | VOLUME 26 NO. 3 GUEST EDITORIAL CONTINUED FROM PAGE 2 Fig. 2 System GPU count, system transistor count, and system GPU area versus GPU architecture year. seamlessly and at high-speed, allowing for integration of GPUs on a datacenter scale. Figure 2 showcases the exponential growth in GPU counts, their corresponding transistor counts, and GPU areas at the system level. GPUs have evolved from single, monolithic devices measured in hundreds of millimeters squared to massive, roomsized arrays measured at tens of meters squared with quadrillions of transistors. This advancement requires exceptionally low levels of latent defects to maintain system dependability. The industry now faces a shift toward even stricter defectivity standards requiring enhanced failure analysis (FA) tools and techniques to detect and root cause the slightest of defects. Large-scale GPT models, which rely on quadrillions of operations per second, exert immense pressure on both the silicon and the system. The silicon used in datacenters must now be unprecedentedly robust and the industry must collaborate closely to overcome this challenge. The silicon providers will need to be even more diligent in defining the technology for robustness, identifying performance and reliability weaknesses, and increasing the process window. Designers must proactively incorporate principles of manufacturability, reliability, and debuggability. Products should be defined and screened with ample tolerance for AI operating conditions. Despite these efforts, defects are inevitable and failures will occur, necessitating a concerted response from the FA community. The FA community must innovate and expand its capability to meet the burgeoning demands of the AI era. Success hinges on a deep integration with the fab, design, product, and system data to root cause the most challenging failures. The FA sector must innovate to detect subtle defects amid an almost endless sea of transistors and routing. New process technologies, such as backside power delivery, are emerging and will challenge traditional diagnostic methods, including laser voltage probing. Consequently, there will be a greater need for alternatives, like e-beam and x-ray based techniques. As the industry scales to meet the computing needs of our AI future, the industry is undergoing a hypergrowth cycle for silicon fabs, component manufacturers, and datacenters. The failure analysis community must be prepared to ramp up technical capability and capacity to meet the demand. In addition, labs will need to pursue more automated and repeatable methodologies including the application of AI and machine learning to increase efficiency. The FA community with its diverse expertise is poised to address these challenges of the AI era. The industry’s demand for AI computing is insatiable. As it develops larger, more complex GPUs and systems, a collaborative, industry-wide effort is essential to ensure a smooth transition into the AI era. The FA community will be particularly instrumental in guiding this transformation successfully. REFERENCES 1. A. Krizhevsky, I. Sutskever, and G.E. Hinton: “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM 60, June 2017, p. 84–90, doi.org/10.1145/3065386. 2. ImageNet Large Scale Visual Recognition Challenge 2012, image-net. org/static_files/files/ilsvrc2012.pdf. 3. George Hotz – GPT-4’s Real Architecture is a 220B Parameter Mixture Model with 8 Sets of Weights, youtu.be/WJWHIZoBOj8?si=jargmj5K qaqvvgWu.
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