In less than two years, “AI” has gone from a nice technical curiosity to a financial, industrial, and publicity juggernaut. Beyond the hype and the inflated stock prices, though, what does this mean from technical perspective?

This talk is to discuss some of the trends we have observed over time in the Infrastructure space when it comes to addressing AI applications and workloads.

Artificial intelligence (AI) systems are creating numerous opportunities and challenges for many facets of society, including both security and privacy. For security, AI is proving to be a power tool for both adversaries and defenders. In addition, AI systems and their associated data have to be defended against a wide range of attacks, some of which are unique to AI. The situation with privacy is similar, but the societal concerns are elevated to a point where laws and regulations are already being enacted.

NVM Express® is in the process of standardizing the Live Migration of a controller from one server to another. That work has already ratified TP4165 Tracking LBA Allocation with Granularity and has made significant changes to TP4159 PCIe Infrastructure for Live Migration. This presentation details the new capabilities the standardization that allows a host to seamless migrate a Virtual Machine (VM) and associated resources without affecting the user experience for use in datacenter load balancing and system maintenance.

CXL is intended to interface to a wide variety of devices including memory and accelerators. CXL is certainly gaining momentum as a preferred interface for disaggregated memory while CXL accelerator devices are still in the early stages of development. Computational Storage is one example of an accelerator that is well positioned to achieve additional benefits from CXL. Data residing in Subsystem Local Memory (SLM) on an NVMe device could potentially be accessed with the load/store interface of CXL and maintain coherency with host memory.

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As Machine Learning continues to forge its way into diverse industries and applications, optimizing computational resources, particularly memory, has become a critical aspect of effective model deployment. This session, ""Memory Optimizations for Machine Learning,"" aims to offer an exhaustive look into the specific memory requirements in Machine Learning tasks and the cutting-edge strategies to minimize memory consumption efficiently.