SNIA Developer Conference September 15-17, 2025 | Santa Clara, CA
With the growing trend for PCIe and CXL solutions, there is a need to improve the sideband management path as currently defined using SMBus. The newest SNIA SFF-TA-1009 specification for EDSFF PCIe devices released in January 2023 defined a method to allow I3C upgrade of the management SMBus/I2C bus. Other standards organizations, such as OCP and PCI SIG, are looking into adopting the same design. OCP and PCI‑SIG agreed that a backward-compatible upgrade of SMBus was required (see Composable Security Architectures call to action at OCP Global Summit 2021, PCI SIG agreed to this in 2020).
We are presenting how important this upgrade is to solve the technical challenges that the new use cases create for SMBus architecture (example new use cases are MCTP protocol, SPDM-based attestation, telemetry reporting, or other asynchronous communications). We are showing how these challenges can be overcome by employing I3C-based solutions that have been designed from the very beginning with the new use cases in mind (the authors of this presentation co-authored the key specifications in this area: MIPI I3C, DMTF MCTP I3C Binding, I3C HUB design, NVMe-MI, EDSFF, and a newly proposed method to read VPD data over I3C). We are also showing how the I3C solution, which is backward-compatible with the legacy SMBus-only devices, actually improves operation of such legacy devices when they are attached to an I3C-capable system.
This paper presents the experimental results that validate the solution, including advanced topics such as polling mode, and includes the conclusions coming from the electrical and topology analysis. Intel and Solidigm partnered to implement the first I3C solution using off-the-shelf components from Aspeed, Renesas, and Microchip. This collaboration generated the experimental results demonstrating how SMBus fails to deliver the necessary functions and the value that comes with I3C upgrade.
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. The organization is also fostering an open chiplet ecosystem by offering high-bandwidth, low-latency, power-efficient, and cost-effective on-package connectivity between chiplets. The UCIe 1.0 specification provides a fully defined stack that comprehends plug-and-play interoperability of chiplets on a package — similar to the seamless interplay on board with off-package interconnect standards such PCI Express®, Universal Serial Bus (USB)®, and Compute Express Link™ (CXL™). This presentation explores the industry demand that brought about the UCIe specification and shares how end-users can easily mix and match chiplet components provided by a multi-vendor ecosystem for System-on-Chip (SoC) construction, including customized SoC.
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). To achieve high data transfer rates with low latency, PCIe 6.0 technology adds innovative new features like Pulse Amplitude Modulation with 4 levels (PAM4) signaling, low-latency Forward Error Correction (FEC) and Flit-based encoding. PCIe 6.0 technology is an optimal solution to meet the demands of Artificial Intelligence and Machine Learning applications, which often require high data bandwidth, low latency transport channels. This presentation will explore the benefits of PCIe 6.0 architecture for storage and AI/ML workloads and its impact on next-generation cloud data centers. Attendees will also learn about the potential AI/ML use cases for PCIe 6.0 technology. Finally, the presentation will provide a preview of what is coming next for PCIe specifications.
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. One purpose of AI democratization is to converge the software and hardware infrastructure and unified data processing and training on the same cluster, where a high-performance, scalable data platform will be a foundational component. In this session, we will introduce motivations and challenges of AI democratization, then we will propose a data platform architecture for E2E AI systems, from software and hardware infrastructure perspectives. It includes distributed compute and storage platform, parallel data processing and connector to deep learning training framework. We will also showcase how this data platform improved the pipeline efficiency of democratized AI solutions on commodity CPU cluster for several recommender system workloads like DLRM, DIEN, and WnD with orders of magnitude performance speedup.
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. By removing the I/O bottlenecks in transaction processing, RAINBLOCK allows miners to process more transactions in the same amount of time. RAINBLOCK makes two novel contributions: the RAINBLOCK architecture that removes I/O from the critical path of processing transactions (txs), and the distributed, multi-versioned DSM-TREE data structure that stores the system state efficiently. We evaluate RAINBLOCK using workloads based on public Ethereum traces (including smart contracts). We show that a single RAINBLOCK miner processes 27.4K txs per second (27× higher than a single Ethereum miner). In a geo-distributed setting with four regions spread across three continents, RAINBLOCK miners process 20K txs per second.
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.