Join us for a talk on how SmartNICs and RDMA Power AI in the Cloud, check out an Embodied AI demo and get insights into the state of the Chapter. Training modern Large Language Models (LLMs) requires tens of thousands of GPUs acting as a single “AI Supercomputer.” To build this “AI Hypercomputer,” we must first address the CPU bottlenecks of traditional general-purpose networking. This talk begins by analyzing why standard TCP/IP processing limits Model Training performance and introduces the concept of “Kernel Bypass” and the role of SmartNICs in offloading network processing from the host CPU. We will explore why modern AI clusters have moved toward hardware offloads (like RDMA) to achieve the high throughput and low latency required for GPU-to-GPU communication. We will also discuss the specific challenges of running lossless transport protocols over lossy Ethernet, where congestion and packet drops can cause severe performance degradation (“tail latency”) in large-scale training jobs. The session concludes by analyzing the architectural design patterns required to optimize flow control and ensure reliable delivery in massive AI infrastructure environments. Demo: Comparing Reinforcement Learning with Imitation Learning for Autonomous Warehouse Pick-and-Place using a Robotic Arm This demo simulates a last-meter warehouse picking task, inspired by Amazon/Kiva-style systems but using general-purpose robotics. The experiments explicitly contrast policy-gradient reinforcement learning methods such as PPO with imitation learning inside a physically realistic embodied-AI task built with Isaac Sim. The demo has been designed to expose where each algorithm struggles or excels due to action spaces, partial observability, contact dynamics, and reward structure. These are core issues in embodied AI. This event features a leading industry expert from Google addressing this important topic, followed by a demo on Embodied AI using Isaac Sim / Lab updates on the state of our chapter from the IEEE CIS SCV Chair. 🎤 Talk 1 The Infrastructure of AI: How SmartNICs and RDMA Power the Cloud Speaker: Sujithra Periasamy, Google 🎤 Demo and Talk Comparing Model-Free RL Algorithms for Autonomous Warehouse Pick-and-Place with Mobile Manipulation Speakers: Mayank Kapadia and Dr. Vishnu S. Pendyala, Department of Applied Data Science, College of Information, Data, and Society, San Jose State University 🎤 Talk 2 State of the Chapter Speaker: Dr. Vishnu S. Pendyala, Chair, IEEE CIS Santa Clara Valley Chapter Co-sponsored by: Vishnu S. Pendyala, San Jose State University Speaker(s): Sujithra Periasamy, Dr. Vishnu S Pendyala Room: MLK Room 225, Dr. Martin Luther King, Jr. Library (SJSU), 150 E San Fernando St San Jose, California 95112, San Jose, California, United States, Virtual: https://events.vtools.ieee.org/m/537154
