MIT AI and Cloud Workshop
Friday, September 29, 2023
Kiva Room, CSAIL, MIT
Cambridge, MA
8:30AM – 5PM EDT

The IAP MIT Workshop on the Future of AI and Cloud Computing Applications and Infrastructure was conducted on Friday September 29, 2023 at the Computer Science and Artificial Intelligence Laboratory, MIT in Cambridge, MA.

Venue: Kiva Room in Building 32 (Room 32G-449), Stata Center, 32 Vassar St., MIT, Cambridge, MA
Time: 8:30AM–5PM
This event was co-organized by Professor Christina Delimitrou and the IAP.

Agenda - Videos of Presentations

Please see Abstracts and Speaker Bios below the Agenda.

8:30-8:55 – Badge Pick-up – Coffee/Tea and Breakfast Food/Snacks
8:55-9:00 – Welcome - Prof. Christina Delimitrou, MIT
9:00-9:30 – Dr. Carole-Jean Wu, Meta, "Scaling AI Computing Sustainably"
9:30-10:00 – Prof. Joel Emer, MIT and Nvidia, "Einsums, Fibertrees and Dataflow: Architecture for the Post-Moore Era"
10:00-10:30 – Prof. Vijay Janapa Reddi, Harvard, "Architecture 2.0: Why Architects Need a Data-centric AI Gymnasium"
10:30-11:00 – Dr. Richard Kessler, CTO Security & Advanced Technology, Marvell, "AI, Cloud, and Marvell Semiconductor"
11-11:30 – Lightning Round for Student Posters
11:30-12:30 – Lunch and Poster Viewing
12:30-1:00 – Prof. Song Han, MIT, "TinyChat for On-device LLM"
1:00-1:30 – Prof. Manya Ghobadi, MIT, "Next-Generation Optical Networks for Machine Learning Jobs"
1:30-2:00 – Prof. Daniel Sanchez, MIT, "A Hardware and Software Architecture to Accelerate Computation on Encrypted Data"
2:00-2:30 – Break
2:30-3:00 – Sundar Dev, Google, "AI-powered infrastructure for the AI-driven future"
3:00-3:30 – Prof. Christina Delimitrou, MIT, "Designing the Next Generation Cloud Systems: To ML or not to ML"
3:30-4:00 – Dr. Jiaqi Gao, Alibaba, "Towards a 100,000-GPU Machine Learning Infrastructure"
4:00-5:00 – Reception and Best Poster Award

Abstracts and Speaker Bios

Prof. Christina Delimitrou, MIT, "Designing the Next Generation Cloud Systems: To ML or not to ML"
Abstract: Cloud systems are experiencing significant shifts both in their hardware, with an increased adoption of heterogeneity, and their software, with the prevalence of microservices and serverless frameworks. These trends require fundamentally rethinking how the cloud system stack should be designed.
In this talk, I will briefly describe the challenges these hardware and software trends introduce, and discuss how applying machine learning (ML) to hardware design, cluster management, and performance debugging can improve the cloud’s performance, efficiency, predictability, and ease of use, as well as cases where alternative techniques to ML work better. I will first present Sage, a performance debugging system that leverages ML to identify and resolve the root causes of performance issues in cloud microservices.

Bio: Christina Delimitrou is an Associate Professor at MIT, focusing on improving the performance, predictability, and resource efficiency of large-scale cloud infrastructures by revisiting the way they are designed and managed.

Sundar Dev, Google, "AI-powered infrastructure for the AI-driven future"
Abstract: The field of computer science and engineering is currently experiencing a period of great excitement. We are entering a new era of computing, driven by advances in artificial intelligence and machine learning. In this talk, I will discuss how Google is tackling the challenges of an AI-driven future by using AI to power our large-scale computing infrastructure.

Bio: Sundar is a performance engineer at Google, focusing on improving the efficiency of the distributed compute infrastructure that enables Google's user-facing software services.

Prof. Joel Emer, MIT, "Einsums, Fibertrees and Dataflow: Architecture for the Post-Moore Era"
Abstract: Over the past few years, efforts to address the challenges of the end of Moore's Law has led to significant rise in domain-specific accelerators. This talk will provide a systematic approach to characterize the range of sparse tensor accelerators.

Bio: Joel Emer has held various research and advanced development positions investigating processor microarchitecture and developing performance modeling techniques. He is currently a professor at MIT.

Dr. Jiaqi Gao, Alibaba, "Towards a 100,000-GPU Machine Learning Infrastructure"
Abstract: Recent advances in Large Language Models have revolutionized the way people interact with machines. This talk will present the recent progress in the data center infrastructure for training LLM models.

Bio: Jiaqi Gao is a researcher at Alibaba, where he works on large-scale machine learning systems.

Prof. Manya Ghobadi, MIT, "Next-Generation Optical Networks for Machine Learning Jobs"
Abstract: This talk explores three elements of designing next-generation machine learning systems: congestion control, network topology, and computation frequency.

Bio: Manya Ghobadi is faculty at MIT, focusing on optical reconfigurable networks and high-performance cloud infrastructure.

Prof. Song Han, MIT, "TinyChat for On-device LLM"
Abstract: Deploying large language models on the edge is demanding. I’ll introduce LLM quantization techniques that can quantize LLM weights to 4bit without losing accuracy.

Bio: Song Han is an associate professor at MIT focusing on making deep learning efficient for IoT devices.

Prof. Vijay Janapa Reddi, Harvard, "Architecture 2.0: Why Architects Need a Data-centric AI Gymnasium"
Abstract: This talk delves into the major challenges and emphasizes the necessity of establishing a shared ecosystem for ML-aided systems and architecture research.

Bio: Vijay Janapa Reddi is an associate professor at Harvard University, specializing in developing mobile computing platforms.

Dr. Richard Kessler, CTO Security & Advanced Technology, Marvell, "AI, Cloud, and Marvell Semiconductor"
Abstract: Marvell data infrastructure products span compute, communication, and storage needs of AI & Cloud systems.

Bio: Rick Kessler is CTO of Security & Advanced Technology at Marvell.

Prof. Daniel Sanchez, MIT, "A Hardware and Software Architecture to Accelerate Computation on Encrypted Data"
Abstract: I will describe a hardware and software stack that tackles the challenges of Fully Homomorphic Encryption (FHE).

Bio: Daniel Sanchez is a professor at MIT's Electrical Engineering and Computer Science Department specializing in large-scale multicores.

Dr. Carole-Jean Wu, Meta, "Scaling AI Computing Sustainably"
Abstract: I will talk about the carbon footprint of AI computing by examining the model development cycle.

Bio: Carole-Jean Wu is a research director at Meta AI, focusing on efficient AI execution.