With the complexity of applications increasing every day, the workloads generated by these applications are complicated and hard to replicate in test environments. We propose an efficient method to synthesize a close approximation of these application workloads based on analyzing the historic autosupport data from field using an iterative mechanism and also a method to store and replay these workloads in the test environment for achieving the goals of customer driven testing.

Object storage systems provide significant value for storing and managing data. The nature of data stored in object systems opens up opportunities to get more value out of these systems than the common expectations of cost reduction, ease of use, resilience, and durability. Maintaining the metadata for large unstructured data sets is difficult and can be time consuming. The system I propose here is an add on engine that adds Artificial Intelligence inferencing functionality to object storage systems.

The recent uptick in generative artificial intelligence (GAI) has put the more pressure on hardware vendors to reduce the carbon footprint of running these power hungry large language models (LLM) in the datacenter. One way to accomplish a lower in-silicon power profile is to break the Von-Neumann bottleneck by tightly integrating traditional SRAM memory cells with interleaved programable processors in the same die.

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.