Compute, memory, storage, and connectivity demands are forcing the industry to adapt as it meets the expanding needs of cloud, edge, enterprise, 5G, and high-performance computing. UCIe — Universal Chiplet Interconnect Express — is an open industry standard founded by the leaders in semiconductors, packaging, IP suppliers, foundries, and cloud service providers to address customer requests for more customizable package-level integration.

For the past three decades, PCI-SIG® has delivered a succession of industry-leading PCI Express® (PCIe®) specifications that remain ahead of the increasing demand for a high-bandwidth, low-latency interconnect for compute-intensive systems in diverse market segments, including data centers, Artificial Intelligence and Machine Learning (AI/ML), high-performance computing (HPC) and storage applications. In early 2022, PCI-SIG released the PCIe 6.0 specification to members, doubling the data rate of the PCIe 5.0 specification to 64 GT/s (up to 256 GB/s for a x16 configuration).

Modern AI systems usually require diverse data processing and feature engineering at a tremendous scale and employ heavy and complex deep learning model that requires expensive accelerators or GPUs. This leads to the typical design of running data processing and AI on two separate platforms, which leads to severe data movement issues and creates big challenges for efficient AI solutions.

AI/ML is not new, but innovations in ML models development have made it possible to process data at unprecedented speeds. Data scientists have used standard POSIX file systems for years, but as the scale and need for performance have grown, many face new storage challenges. Samsung has been working with customers on new ways of approaching storage issues with object storage designed for use with AI/ML. Hear how software and hardware are evolving to allow unprecedented performance and scale of storage for Machine Learning.

We present RAINBLOCK, a public blockchain that achieves high transaction throughput without modifying the proof-of-work consensus. The chief insight behind RAINBLOCK is that while consensus controls the rate at which new blocks are added to the blockchain, the number of transactions in each block is limited by I/O bottlenecks. Public blockchains like Ethereum keep the number of transactions in each block low so that all participating servers (miners) have enough time to process a block before the next block is created.

The next generation of automobiles moves to the adoption of PCIe for data communications in vehicles, and the JEDEC Automotive SSD solution enables a high performance, high reliability solution for this shared centralized storage. Features such as SR/IOV highlight the requirements of these computers on wheels with multiple SoC functions for vehicle control, sensors, communications, entertainment, and artificial intelligence.

In this session I will start with identifying the current problems in Artificial Intelligence as data exists in most industries/edge in the form of isolated pockets with privacy constraints for sharing the data for training an AI model. While transferring data from edge storage to a centralized cloud for training -many problems arise. Performance issues, cost and security or regulatory compliance are some of them. I’ll then walk through detailed features of federated learning combined with Blockchain to solve the above mentioned problems .

Gary Grider will kick off the talk with a brief background of the challenges currently faced in mass storage systems, and why it is so difficult for modern S3 based workloads to utilize them. Ben McClelland, who architected the system and did the bulk of the development, will introduce the Versity Gateway, an open-source high-performance object-to-file translation tool. He will explain that the Versity Gateway was written from scratch in Go, highlighting the benefits of this choice.

Emerging Deep Learning/ Machine learning and cloud native Applications at data center scale demand terabytes of data flowing across the storage/ memory hierarchy, straining interconnect bandwidth and component capacities. The Industry has responded with a wide range of solutions like process node shrink, higher capacity devices, new tiers, innovative form factors, new interconnect technologies and fabrics, new types of compute architectures, new algorithms and more to creatively leverage storage/memory tiering.

High-performance workloads demand on-package integration of heterogeneous processing units, on-package memory, and communication infrastructure to meet the demands of the emerging compute landscape. Applications such as artificial intelligence, machine learning, data analytics, 5G, automotive, and high-performance computing are driving these demands to meet the needs of cloud computing, intelligent edge, enterprise, and client computing infrastructure.