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Events for March 30, 2023

  • ECE-S Seminar: Dr. Priyanka Raina

    ECE-S Seminar: Dr. Priyanka Raina

    Thu, Mar 30, 2023 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Priyanka Raina, Assistant Professor of Electrical Engineering, Stanford University

    Talk Title: Agile Design of Domain-Specific Accelerators and Compilers

    Abstract: With the slowing of Moore's law, computer architects have turned to domain-specific hardware accelerators to improve the performance and efficiency of computing systems. However, programming these systems entails significant modifications to the software stack to properly leverage the specialized hardware. Moreover, the accelerators become obsolete quickly as the applications evolve. What is needed is a structured approach for generating programmable accelerators and for updating the software compiler as the accelerator architecture evolves with the applications. In this talk, I will describe a new agile methodology for co-designing programmable hardware accelerators and compilers. Our methodology employs a combination of new programming languages and formal methods to automatically generate the accelerator hardware and its compiler from a single specification. This enables faster evolution and optimization of accelerators, because of the availability of a working compiler. I will showcase this methodology using Amber, a coarse-grained programmable accelerator for imaging and machine learning (ML) we designed and fabricated using our flow in TSMC 16 nm technology. I will show how we agilely evolved Amber into Onyx, our next generation accelerator, using an application-driven design space exploration framework called APEX enabled by our hardware-compiler co-design flow.

    Biography: Priyanka Raina is an Assistant Professor of Electrical Engineering at Stanford University. She received her B.Tech. degree in Electrical Engineering from the IIT Delhi in 2011 and her S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2013 and 2018. Priyanka's research is on creating high-performance and energy-efficient architectures for domain-specific hardware accelerators in existing and emerging technologies. She also works on methodologies for agile hardware-software co-design. Her research has won best paper awards at VLSI, ESSCIRC and MICRO conferences and in the JSSC journal. She has also won the NSF CAREER Award, the Intel Rising Star Faculty Award, Hellman Faculty Scholar Award and is a Terman Faculty Fellow.

    Host: Dr. Murali Annavaram, annavara@usc.edu

    Webcast: https://usc.zoom.us/j/93842345540?pwd=V3U1TUgwK2pyTE9BWThDeCtxbDJOdz09

    More Information: ECE Seminar Announcement-Raina, Priyanka-033023.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    WebCast Link: https://usc.zoom.us/j/93842345540?pwd=V3U1TUgwK2pyTE9BWThDeCtxbDJOdz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • NL Seminar-Getting AI to Do Things I Can't

    Thu, Mar 30, 2023 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Ruiqi Zhong, Cal-Berekely

    Talk Title: Getting AI to Do Things I Can't

    Series: NL Seminar

    Abstract: REMINDER

    Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.

    If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.

    Is it possible to tame powerful AI systems even when we struggle to determine the ground truth ourselves? In this talk, I will cover two example NLP tasks 1. automatically searching for goal-relevant patterns in large text collections and explaining them to humans in natural language 2. labeling complex SQL programs using non-programmers with the aid of our AI system and achieving accuracy on par with database experts. In both cases, we build tools that help humans scrutinize the AI's behavior with high effectiveness but low effort, bringing new insights that human experts have not anticipated.

    Biography: Ruiqi Zhong is a 4th year Ph.D. student advised by Jacob Steinhardt and Dan Klein, working on NLP and AI Alignment.

    Ruiqi Zhong attends th Univ. of California Berkeley, he is working on NLP and AI Alignment. His research aims to enable humans to effectively supervise AI systems on tasks where the ground truth is hard to obtain. He reads about epistemology and labor economy in his spare time.

    Host: Jon May and Justin Cho

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://www.youtube.com/watch?v=dHkYN33TtLM

    Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689

    WebCast Link: https://www.youtube.com/watch?v=dHkYN33TtLM

    Audiences: Everyone Is Invited

    Contact: Pete Zamar

    Event Link: https://nlg.isi.edu/nl-seminar/

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  • CS Colloquium: Shuang Li (MIT) - Enabling Compositional Generalization of AI Systems

    Thu, Mar 30, 2023 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Shuang Li, Massachusetts Institute of Technology (MIT)

    Talk Title: Enabling Compositional Generalization of AI Systems

    Series: CS Colloquium

    Abstract: A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge. Despite their impressive performance, current AI systems fall short in this area and are often unable to solve tasks that fall outside of their training distribution. My research aims to bridge this gap by incorporating compositionality into deep neural networks, thereby enhancing their ability to generalize and solve novel and complex tasks, such as generating 2D images and 3D assets based on complicated specifications, or enabling humanoid agents to perform a diverse range of household activities. The implications of this work are far-reaching, as compositionality has numerous applications across fields such as biology, robotics, and art production. By significantly improving the compositionality ability of AI systems, this research will pave the way for more data-efficient and powerful models in different research areas.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Shuang Li is a Ph.D. Candidate at MIT, advised by Antonio Torralba. She is interested in developing AI systems that generalize to a wide range of novel tasks and continually learn from the environment. Her research explores methods to incorporate compositionality into deep learning models, giving rise to stronger generalization abilities for solving more challenging novel tasks. Her research involves Generative Modeling, Embodied AI, and Vision-Language Understanding. Shuang is a recipient of the Meta Research Fellowship, Adobe Research Fellowship, MIT Seneff-Zue CS Fellowship, EECS Rising Star, ICML Outstanding Reviewer, and best and outstanding paper awards at NeurIPS workshops.

    Host: Swabha Swayamdipta

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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