Discover how SNIA is redefining the future of data through collaboration and innovation. Join Scott Shadley, Jason Molgaard, and Shyam Iyer to hear how SNIA's initiatives, particularly in accelerating data, are blending storage solutions with data-centric strategies to drive the industry forward. The conversation highlights the significance of standardization in data management, emphasizing the importance of collaboration among competitors.

Imagine a world where your data could last thousands of years without requiring even a watt of energy. The world is literally running out of data storage, and DNA may be the answer. DNA exceeds many times the storage density of magnetic tape or solid-state media and can store hundreds of petabytes in a mere gram of DNA!

Will AI revolutionize the way businesses operate and the way we live? Hear AI pioneer Dr. Rita Wouhaybi, an expert focused on AI, share her excitement over the rapid advances in AI technology. She explains why she is obsessed with the concept of Edge AI and how Edge breaks down some of the fundamental assumptions that we have had about AI for years.

Unlocking a Sustainable Future for Data Storage In a world of surging data demands, how can we reduce the environmental toll of storage solutions? Discover the power of the circular economy to reshape the storage industry for a greener tomorrow with this webinar, featuring Jonmichael Hands (Co-Chair SNIA SSD SIG and Board Member, Circular Drive Initiative) and Shruti Sethi (Sr. PM at Microsoft and Leadership Team, Open Compute Project-Sustainability).

New Memories like MRAM, ReRAM, PCM, or FRAM are vying to replace embedded flash and, eventually, even embedded SRAM. Are there other memory technologies threatened with similar fates? What will the memory market look like in another 20 years?

This presentation examines the critical role of storage solutions in optimizing AI workloads, with a primary focus on storage-intensive AI training workloads. We will highlight how AI models interact with storage systems during training, focusing on data loading and checkpointing mechanisms. We will explore how AI frameworks like PyTorch utilize different storage connectors to access various storage solutions. Finally, the presentation will delve into the use of file-based storage and object storage in the context of AI training:

With the increased business value that AI enabled applications can unlock, there is a need to support Gen AI models at varying degrees of scale - from foundational model training in data centers to inference deployment on edge and mobile devices.

Main memory dominates data center server cost, and hence, data center operators are exploring alternative technologies such as CXL-attached memory to improve cost without jeopardizing performance.

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.