November 2023 - talks - lists.cs.princeton.edu

Ziyang Xu will present his Pre FPO "PROMPT: A Fast and Extensible Memory Profiling Framework" on December 11, 2023 at 10am in CS 302.
by Gradinfo 06 Nov '23

06 Nov '23

Ziyang Xu will present his Pre FPO "PROMPT: A Fast and Extensible Memory Profiling Framework" on December 11, 2023 at 10am in CS 302. Committee: Prof. David I. August (Advisor) Prof. Amit Levy (Examiner) Prof. Aarti Gupta (Examiner) Prof. Zachary Kincaid (Reader) Prof. Simone Campanoni (Reader, Associate Professor at Northwestern University) All are welcome to attend. PROMPT: A Fast and Extensible Memory Profiling Framework Abstract: Memory profiling captures programs' dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique program trace summary, various memory profiler types have been developed. Yet, designing practical memory profilers often requires extensive compiler expertise, adeptness in program optimization, and significant implementation efforts. This often results in a void where aspirations for fast and robust profilers remain unfulfilled. To bridge this gap, this dissertation presents PROMPT, a pioneering framework for streamlined development of fast memory profilers. With it, developers only need to specify profiling events and define the core profiling logic, bypassing the complexities of custom instrumentation and intricate memory profiling components and optimizations. Two state-of-the-art memory profilers were ported with PROMPT while all features preserved. By focusing on the core profiling logic, the code was reduced by more than 65% and the profiling speed was improved by 5.3x and 7.1x respectively. To further underscore PROMPT's impact, a tailored memory profiling workflow was constructed for a sophisticated compiler optimization client. In just 570 lines of code, this redesigned workflow satisfies the client's memory profiling needs while achieving more than 90% reduction in profiling time and improved robustness compared to the original profilers. PROMPT overcomes the limitations of traditional compile-time instrumentation memory profilers by integrating dynamic binary instrumentation capabilities. It automatically infers the characteristics of external function calls, significantly reducing dependence on binary instrumentation and enhancing performance. Memory profiling events generated by both methods are formatted consistently, ensuring that modules developed with PROMPT are adaptable and retain their functionality irrespective of the instrumentation technique employed. In acknowledgment of the complexity inherent in memory behaviors and profiler implementations, PROMPT utilizes a novel testing technique. This method involves inverting memory analysis algorithms to generate synthetic programs with known memory behaviors. Such a strategy is instrumental in detecting and rectifying errors in memory profiler implementations created with PROMPT, thus bolstering confidence in the accuracy of the profilers.

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Nov 8: LLM Forum with Wai Chee Dimock (RSVP)
by Emily C. Lawrence 06 Nov '23

06 Nov '23

The Princeton LLM Forum continues next Wednesday! Please join us for a conversation with Wai Chee Dimock (William Lampson Professor Emeritus of American Studies and English at Yale University and research affiliate at the Harvard University Center for the Environment) on the topic of culture! This session's Princeton respondent will be Meredith Martin (Associate Professor of English and Faculty Director of CDH ). Nov 8, 4:30 – 6:00 pm Friend Center 101 RSVPs encouraged R eception to follow [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | Add to Calendar ] | [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | More info ] | [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | RSVP ] Wai Chee Dimock writes about public health, climate change, and indigenous communities, focusing on the symbiotic relation between human and nonhuman intelligence. She is now at Harvard’s Center for the Environment, working on a new book, “AI, Microbes, and Us: Risky Partners in an Age of Pandemics and Climate Change.” A collaborative project, “AI for Climate Resilience,” is co-sponsored by Stanford’s Institute for Human-Centered Artificial Intelligence and Yale’s Jackson School of Global Affairs. Dimock’s most recent book is Weak Planet (2020). Other books include Through Other Continents: American Literature Across Deep Time (2006); Shades of the Planet (2007); and a team-edited anthology, American Literature in the World: Anne Bradstreet to Octavia Butler (2017). Her 1996 book, Residues of Justice: Literature, Law, Philosophy , was reissued in a new edition in 2021. Her essays have appeared in Artforum , The Chronicle of Higher Education , The Hill , the Los Angeles Review of Books , The New York Times , The New Yorker , and Scientific American . Missed the lively [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | conversation with Meredith Whittaker ] and Arvind Narayanan? Catch up at the link below. [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | Watch 10/25 Recording ]

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DeCenter Seminar: Ling Ren, Nov 15 at 4:30pm
by Emily C. Lawrence 06 Nov '23

06 Nov '23

DeCenter Seminar Speaker: Ling Ren, University of Illinois at Urbana-Champaign Talk Title: What insights can we learn from the Bitcoin Algorithm? November 15, 2023 4:30pm talk followed by a reception Friend 113 Convocation Room [ https://decenter.princeton.edu/events/what-insights-can-we-learn-from-the-b… | https://decenter.princeton.edu/events/what-insights-can-we-learn-from-the-b… ] Abstract: Bitcoin's longest-chain algorithm, also known as Nakamoto consensus, is a novel solution to the classic Byzantine fault-tolerant (BFT) consensus problem. What is fundamentally novel about it, and what algorithmic insights can we learn from it? In this talk, I will discuss some insights and inspirations we gained from Bitcoin that led to new advances in BFT consensus. First, Nakamoto consensus supports dynamic participation. The Bitcoin system has operated for over a decade, during which it has witnessed a rapid increase and sudden drops in the participation level. But to support this phenomenal level of robustness, longest-chain protocols suffer from long latency as a fundamental trade-off. Classic BFT protocols, on the other hand, can achieve constant latency but cannot make progress under dynamic participation. I will present a protocol that simultaneously supports dynamic participation and achieves constant latency. The core technique is to extend the classic BFT approach from static quorum size to dynamic quorum size according to the current participation level. Second, Bitcoin uses a sparse peer-to-peer network to reduce communication. This ingredient loosely inspired us to design a Byzantine agreement protocol tolerating minority faults with quadratic communication. This meets the well-known Dolev-Reischuk lower bound and closes a 40-year open problem on the optimal communication complexity of Byzantine agreement. Third, Bitcoin allows individual clients to make their own assumptions about the network and opt for different security-efficiency trade-offs. We propose the notion of flexible Byzantine fault tolerance to capture and generalize this property. If time permits, I will also briefly discuss other core features of Nakamoto consensus that may inspire important future research. Bio : Ling Ren is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Prior to joining the University of Illinois, he obtained his Ph.D. from MIT and worked at VMware Research. He has won several awards, including NSF CAREER Award, Google Research Scholar Award, VMware Early Career Faculty Grant, Bitcoin Research Prize, Top Picks in Hardware and Embedded Security, and Best Paper Runner-Up and Best Student Paper at CCS.

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ECE Korhammer Seminar: Fei Miao, UConn, Tues Nov. 14
by Emily C. Lawrence 06 Nov '23

06 Nov '23

ECE KORHAMMER SEMINAR SERIES Speaker: Fei Miao, University of Connecticut Title: Learning and Control for Safety, Efficiency, and Resiliency of Embodied AI Day: November 14, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Hosts: Jaime Fernández Fisac and Chi Jin Abstract: With rapid evolution of sensing, communication, and computation, integrating learning and control presents significant Embodied AI opportunities. However, current decision-making frameworks lack comprehensive understanding of the tridirectional relationship among communication, learning and control, posing challenges for multi-agent systems in complex environments. In the first part of the talk, we focus on learning and control with communication capabilities. We design an uncertainty quantification method for collaborative perception in connected autonomous vehicles (CAVs). Our findings demonstrate that communication among multiple agents can enhance object detection accuracy and reduce uncertainty. Building upon this, we develop a safe and scalable deep multi-agent reinforcement learning (MARL) framework that leverages shared information among agents to improve system safety and efficiency. We validate the benefits of communication in MARL, particularly in the context of CAVs in challenging mixed traffic scenarios. We incentivize agents to communicate and coordinate with a novel reward reallocation scheme based on Shapley value for MARL. Additionally, we present our theoretical analysis of robust MARL methods under state uncertainties, such as uncertainty quantification in the perception modules or worst-case adversarial state perturbations. In the second part of the talk, we briefly outline our research contributions on robust MARL and data-driven robust optimization for sustainable mobility. We also highlight our research results concerning CPS security. Through our findings, we aim to advance Embodied AI and CPS for safety, efficiency, and resiliency in dynamic environments. Bio: Fei Miao is Pratt & Whitney Associate Professor of the Department of Computer Science and Engineering, a Courtesy Faculty of the Department of Electrical & Computer Engineering, University of Connecticut, where she joined in 2017. She is affiliated to the Institute of Advanced Systems Engineering and Eversource Energy Center. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of UPenn from 2016 to 2017. She received Ph.D. degree and the Best Doctoral Dissertation Award in Electrical and Systems Engineering, with a dual M.S. degree in Statistics from the University of Pennsylvania in 2016. She received the B.S. degree in Automation from Shanghai Jiao Tong University in 2010. Her research focuses on multi-agent reinforcement learning, robust optimization, uncertainty quantification, and game theory, to address safety, efficiency, robustness, and security challenges of Embodied AI and CPS, for systems such as connected autonomous vehicles, sustainable and intelligent transportation systems, and smart cities. Dr. Miao is a receipt of the NSF CAREER award and a couple of other awards from NSF. She received the Best Paper Award and Best Paper Award Finalist at the 12th and 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2021 and 2015, Best paper Award at the 2023 AAAI DACC workshop, respectively. This seminar is supported with funds from the Korhammer Lecture Series.

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Bhargav Reddy Godala will present his Pre FPO "Criticality Aware Front-End" on November 6th, 2023 in Friend Center 125 at 10:30am.
by Gradinfo 01 Nov '23

01 Nov '23

Bhargav Reddy Godala will present his Pre FPO "Criticality Aware Front-End" on November 6th, 2023 in Friend Center 125 at 10:30am. The members of his committee are as follows: Examiners: Prof. David August (adviser) Prof. Margaret Martonosi Prof. David Wentzlaff Readers: Gilles Pokam (Intel) Svilen Kanev (Google) (approved by Grad School) Prof. David August (adviser) Talk title and abstract follow below. Title: Criticality Aware Front-End Abstract: Code footprints continue to grow faster than instruction caches, putting additional pressure on existing front-end structures like iCaches, BTBs, and iTLBs. Even with aggressive decoupled front-ends and accurate fetch-directed instruction prefetching (FDIP), modern processors experience significant front-end stalls. To address this, this thesis proposes Priority Directed Instruction Prefetching (PDIP), a novel instruction prefetching technique that complements FDIP by issuing prefetches for targets along the resteer path where FDIP stalls occur. PDIP identifies these targets and associates them with a trigger for future prefetch. When paired with EMISSARY, a recently proposed cost-aware cache management policy, PDIP achieves a geomean IPC speedup of 3.7% across a set of industry-relevant benchmark programs using a budget of only 43.5KB. PDIP achieves 62% of the ideal prefetching performance.

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MAE Seminar Speaker Monroe Kennedy III, Friday, Nov 3 at 12:30
by Emily C. Lawrence 01 Nov '23

01 Nov '23

Collaborative Robotics: From Dexterity to Teammate Prediction Friday, November 3rd at 12:30 PM Bowen Hall Room 222 MAE Seminar Series [ https://mae.princeton.edu/about-mae/events/collaborative-robotics-dexterity… | https://mae.princeton.edu/about-mae/events/collaborative-robotics-dexterity… ] Prof. Monroe Kennedy III, Stanford University Abstract: There have been amazing advances in the field of robotic autonomy over the past few decades, we’ve seen robots move from factory floors to working examples of autonomous vehicles on public roads. However, there is still a notable barrier to having robots in everyday environments performing functional tasks. These barriers are usually due to current robots having limited dexterity when it comes to manipulating objects that humans can handle with minimal difficulty, to barriers that exist when a robot is supposed to work alongside a human to complete a task or enable a human to perform a task better. In this talk, we will address the catalyst problem of robotic autonomy in human environments by addressing robotic dexterity and the necessity of improving the sense of robotic touch. We will then look at how robots can become effective teammates by modeling the behavior of their collaborators in the context of the task or environment, which enables them to predict future behavior and take collaborative actions. Bio: Monroe Kennedy is an assistant professor in Mechanical Engineering and by courtesy, Computer Science at Stanford University. Monroe is the recipient of the NSF Faculty Early Career Award. He directs the Assistive Robotics and Manipulation Laboratory (ARMLab), where the focus is on developing collaborative, autonomous robots capable of performing dexterous, complex tasks with human and robotic teammates. Monroe received his Ph.D. in Mechanical Engineering and Applied Mechanics and master’s in Robotics from the University of Pennsylvania.

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