In the new era of AI infrastructure, CMOS scaling remains the workhorse for heavy computational workloads. But the need for an energy-efficient solution imposes a paradigm shift at the interconnect level, requiring an intimate 3D co-integration of advanced ASICs and optical connectivity. 

As the architectural complexity of new products increases, relying on state-of-the-art platforms, with a short path to manufacturing. In this workshop, we will highlight how you can access following technologies for your future products:  

• Advanced-node ASIC down to TSMC N2 
• Imec’s integrated photonics platforms from 200G up to co-packaged optics 
• Imec’s advanced 3D packaging technique from interposer to hybrid bonding 

Location: Room 207

Duration: 1 hour

In this session, we will explore the end-to-end workflow of managing foundation model (FM) development on Amazon SageMaker HyperPod. Our discussion will cover both distributed model training and inference using frameworks like PyTorch and KubeRay. Additionally, we will dive into operational aspects, including system observability and resiliency features for scale and cost-performance using Amazon EKS on SageMaker HyperPod. By the end of this hands-on session, you will gain a robust understanding of training and deploying FMs efficiently on AWS. You will learn to leverage cutting-edge techniques and tools to ensure high performance, reliable, and scalable FM development.

Location: Room 206

Duration: 1 hour

The rapid evolution of high-performance computing (HPC) clusters has been instrumental in driving transformative advancements in AI research and applications. These sophisticated systems enable the processing of complex datasets and support groundbreaking innovation. However, as their adoption grows, so do the critical security challenges they face, particularly when handling sensitive data in multi-tenant environments where diverse users and workloads coexist. Organizations are increasingly turning to Confidential Computing as a framework to protect AI workloads, emphasizing the need for robust HPC architectures that incorporate runtime attestation capabilities to ensure trust and integrity.

In this session, we present an advanced HPC cluster architecture designed to address these challenges, focusing on how runtime attestation of critical components – such as the kernel, Trusted Execution Environments (TEEs), and eBPF layers – can effectively fortify HPC clusters for AI applications operating across disjoint tenants. This architecture leverages cutting-edge security practices, enabling real-time verification and anomaly detection without compromising the performance essential to HPC systems.

Through use cases and examples, we will illustrate how runtime attestation integrates seamlessly into HPC environments, offering a scalable and efficient solution for securing AI workloads. Participants will leave this session equipped with a deeper understanding of how to leverage runtime attestation and Confidential Computing principles to build secure, reliable, and high-performing HPC clusters tailored for AI innovations.

Location: Room 201

Duration: 1 hour

Dive into a hands-on workshop designed exclusively for AI developers. Learn to leverage the power of Google Cloud TPUs, the custom accelerators behind Google Gemini, for highly efficient LLM inference using vLLM. In this workshop, you will build and deploy Gemma 3 27B on Trillium TPUs with vLLM and Google Kubernetes Engine (GKE). Explore advanced tooling like Dynamic Workload Scheduler (DWS) for TPU provisioning, Google Cloud Storage (GCS) for model checkpoints, and essential observability and monitoring solutions

Location: Room 207

Duration: 1 hour