CS Colloquium Speaker

Speaker: Kianté Brantley, Cornell University

Date: Monday, April 1

Time: 12:30pm EST

Location: CS 105

Host: Ryan Adams

Event page: https://www.cs.princeton.edu/events/26611

Title: Learning from Interaction

Abstract: Machine learning systems have seen advancements due to large models pre-trained on vast amounts of data. These pre-trained models have led to progress on various downstream tasks when fine-tuned. However, for machine learning systems to function in real-world environments, they must overcome certain challenges that are not influenced by model or dataset sizes. One potential solution is to fine-tune machine learning models based on online interactions.

In this talk, I will present my research on developing natural language processing systems that learn from interacting in an environment. I will begin by describing the issues that arise when systems are trained on offline data and then deployed in interactive environments. Additionally, I will present an algorithm that addresses these issues using only environmental interaction without additional supervision. Moreover, I will demonstrate how learning from interaction can improve natural language processing systems. Finally, I will present a set of new interactive learning algorithms explicitly designed for natural language processing systems.

Bio: Kianté Brantley is a Postdoctoral Associate in the Department of Computer Science at Cornell University., working with Thorsten Joachims. He completed his Ph.D. in Computer Science at the University of Maryland College Park, advised by Dr. Hal Daumé III. His research focuses on developing machine learning models that can make automated decisions in the real world with minimal supervision. His research lies at the intersection of imitation learning, reinforcement learning, and natural language processing. He is a recipient of the NSF LSAMP BD Fellowship, ACM SIGHPC Computational and Data Science Fellowship, Microsoft Dissertation Research Grant, Ann G. Wylie Dissertation Fellowship, and NSF CIFellow Postdoctoral Fellowship.

CS Colloquium Speaker

Speaker: Saksham Agarwal, Cornell University

Date: Tuesday, April 2

Time: 12:30pm EST

Location: CS 105

Host: Wyatt Lloyd

Event page: https://www.cs.princeton.edu/events/26606

Title: The Host Network (and its implications to network protocols, OS and hardware)

Abstract: The host network enables data transfers within hosts, and forms the “last mile” for data transfers across hosts for distributed applications. This talk will reflect on my (ongoing) journey that started with a surprising phenomenon observed in a lab experiment—nanosecond-scale inefficiencies within the host network percolating through network protocols and OS to create millisecond-scale impact on distributed applications. I will discuss my work on understanding, characterizing, and resolving the above phenomenon in the lab and in production clusters. I will also discuss how this phenomenon opens up intriguing research questions at the intersection of computer networking, OS and architecture.

Bio: Saksham Agarwal is a PhD student in the Computer Science department at Cornell University, advised by Prof. Rachit Agarwal. He did his undergraduate studies at IIT Kanpur. He is a recipient of Google PhD Fellowship, Cornell University Fellowship, a SIGCOMM Best Student Paper Award, and a Cornell CS Outstanding TA Award.

CS Colloquium Speaker

Speaker: Sagar Karandikar, University of California, Berkeley

Date: Wednesday, April 3

Time: 12:30pm EST

Location: CS 105

Host: Margaret Martonosi

Event page: https://www.cs.princeton.edu/events/26592

*Live stream is available to Princeton University ID holders only.

Title: Catch M(oor)e If You Can: Agile Hardware/Software Co-Design for Hyperscale Cloud Systems

Abstract: Global reliance on cloud services, powered by transformative technologies like generative AI, machine learning, and big-data analytics, is driving exponential growth in demand for hyperscale cloud compute infrastructure. Meanwhile, the breakdown of classical hardware scaling (e.g., Moore's Law) is hampering growth in compute supply. Building domain-specific hardware can address this supply-demand gap, but catching up with exponential demand requires developing new hardware rapidly and with confidence that performance/efficiency gains will compound in the context of a complete system. These are challenging tasks given the status quo in hardware design, even before accounting for the immense scale of cloud systems.

This talk will focus on two themes of my work: (1) Developing radical new agile, end-to-end hardware/software co-design tools that challenge the status quo in hardware design for systems of all scales and unlock the ability to innovate on new hardware at datacenter scale. (2) Leveraging these tools and insights from hyperscale datacenter fleet profiling to architect and implement state-of-the-art domain-specific hardware that addresses key efficiency challenges in hyperscale cloud systems.

I will first cover my work creating the award-winning and widely used FireSim FPGA-accelerated hardware simulation platform, which provides unprecedented hardware/software co-design capabilities. FireSim automatically constructs high-performance, cycle-exact, scale-out simulations of novel hardware designs derived from the tapeout-friendly RTL code that describes them, empowering hardware designers and domain experts alike to directly iterate on new hardware designs in hours rather than years. FireSim also unlocks innovation in datacenter hardware with the unparalleled ability to scale to massive, distributed simulations of thousand-node networked datacenter clusters with specialized server designs and complete control over the datacenter architecture. I will then briefly cover my work co-creating the also widely used Chipyard platform for agile construction, simulation (including FireSim), and tape-out of specialized RISC-V System-on-Chip (SoC) designs using a novel, RTL-generator-driven approach.

Next, I will discuss my work in collaboration with Google on Hyperscale SoC, a cloud-optimized server chip built, evaluated, and taped-out with FireSim and Chipyard. Hyperscale SoC includes my work on several novel domain-specific accelerators (DSAs) for expensive but foundational operations in hyperscale servers, including (de)serialization, (de)compression, and more. Hyperscale SoC demonstrates a new paradigm of data-driven, end-to-end hardware/software co-design, combining key insights from profiling Google's world-wide datacenter fleet with the ability to rapidly build and evaluate novel hardware designs in FireSim/Chipyard. This instance of Hyperscale SoC is just the beginning; I will conclude by covering the wide-ranging opportunities that can now be explored for radically redesigning next generation hyperscale cloud datacenters.

Bio: Sagar Karandikar is a Ph.D. Candidate at UC Berkeley and a Student Researcher at Google. His work broadly focuses on co-designing hardware and software to build next generation hyperscale cloud systems. He is also interested in agile, open-source hardware development methodologies.

His first-author publications have received several honors, including being selected for the ISCA@50 25-year Retrospective, as an IEEE Micro Top Pick, as an IEEE Micro Top Pick Honorable Mention, and as the MICRO '21 Distinguished Artifact Award winner.

He created and leads the FireSim project, which has been used as a foundational research platform in over 50 peer-reviewed publications from first authors at over 20 institutions. FireSim has also been used in the development of commercially available chips and as a standard host platform for DARPA and IARPA programs. He is a co-creator and co-lead of the also widely used Chipyard RISC-V System-on-Chip (SoC) development platform. His work on Hyperscale SoC has been influential at Google and more broadly across other silicon vendors. He was selected as a 2022 DARPA Riser and received the UC Berkeley Outstanding Graduate Student Instructor (TA) Award. He received his M.S. and B.S. from UC Berkeley.

CS Colloquium Speaker

Speaker: Yilun Du, Massachusetts Institute of Technology

Date: Thursday, April 4

Time: 12:30pm EST

Location: CS 105

Host: Felix Heide

Event page: https://www.cs.princeton.edu/events/26595

Title: Generalizing Beyond the Training Distribution through Compositional Generation

Abstract: Generative AI has led to stunning successes in recent years but is fundamentally limited by the amount of data available. This is especially limiting in the embodied setting – where an agent must solve new tasks in new environments. In this talk, I’ll introduce the idea of compositional generative modeling, which enables generalization beyond the training data by building complex generative models from smaller constituents. I’ll first introduce the idea of energy-based models and illustrate how they enable compositional generative modeling. I’ll then illustrate how such compositional models enable us to synthesize complex plans for unseen tasks at inference time. Finally, I'll show how such compositionality can be applied to multiple foundation models trained on various forms of Internet data, enabling us to construct decision-making systems that can hierarchically plan and solve long-horizon problems in a zero-shot manner.

Bio: Yilun Du is final year PhD student at MIT CSAIL advised by Leslie Kaelbling, Tomas Lozano-Perez and Joshua Tenenbaum. His research spans the fields of machine learning and robotics, with a focus on generative models. He is supported by the NSFGraduate Research Fellowship and was previously a research fellow at OpenAI, a visiting researcher at FAIR and a student researcher at Google Deepmind.