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Events for April 08, 2022
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Grammar TutorialsFri, Apr 08, 2022 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
INDIVIDUAL GRAMMAR TUTORING FOR VITERBI UNDERGRADUATE AND GRADUATE STUDENTS
Meet one-on-one with Viterbi faculty, build your grammar skills, and take your writing to the next level!
Viterbi faculty from the Engineering in Society Program (formerly the Engineering Writing Program) will help you identify and correct recurring grammatical errors in your academic writing, cover letters, resumes, articles, presentations, and dissertations.
Bring your work, and let's work together to clarify your great ideas!
Contact helenhch@usc.edu with questions.
Location: Zoom
Audiences: Graduate and Undergraduate Students
Contact: Helen Choi
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ECE-EP Seminar - Jae-sun Seo, Friday, April 8th at 10am via Zoom
Fri, Apr 08, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jae-sun Seo, Arizona State University
Talk Title: Energy-Efficient AI Chip Designs with Digital and Analog Circuits
Abstract: AI algorithms have been widespread across many practical applications, e.g. convolutional neural networks (CNNs) for computer vision, long short-term memory (LSTM) for speech recognition, etc., but state-of-the-art algorithms are compute-/memory-intensive, posing challenges for AI hardware to perform inference and training tasks with high throughput and low power consumption, especially on area-/energy-constrained edge devices.
In this talk, I will present our recent research of several energy-efficient AI ASIC accelerators on both all-digital chips and analog/mixed-signal circuit based chips. These include (1) a 40nm CNN inference accelerator with conditional computing and low external memory access, (2) a 28nm CNN training accelerator exploiting dynamic activation/weight sparsity, and (3) a 28nm programmable in-memory computing (IMC) inference accelerator integrating 108 capacitive-coupling-based IMC SRAM macros. We will discuss the digital/analog circuits and architecture design, as well as hardware-aware algorithms employed for the proposed energy-efficient AI accelerators. Based on the demonstrated advantages and challenges of digital and analog AI chip designs, emerging research directions for new AI hardware with new device/circuit/architecture/algorithm design considerations will be discussed.
Biography: Jae-sun Seo received the Ph.D. degree from the University of Michigan, Ann Arbor in 2010. From 2010 to 2013, he was with IBM T. J. Watson Research Center, working on the DARPA SyNAPSE project and next-generation processor designs. Since 2014, he has been with Arizona State University, where he is currently an Associate Professor in the School of ECEE. He was a visiting faculty at Intel Circuits Research Lab in 2015. His research interests include efficient hardware design of machine learning algorithms and neuromorphic computing. Dr. Seo was a recipient of IBM Outstanding Technical Achievement Award (2012), NSF CAREER Award (2017), and Intel Outstanding Researcher Award (2021). He has served on the technical program committees for ISSCC, MLSys, DAC, DATE, ICCAD, etc.
Host: ECE-Electrophysics
More Information: Jae-sun Seo Flyer.pdf
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Advanced Manufacturing Seminar
Fri, Apr 08, 2022 @ 10:00 AM - 11:30 AM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: John Hart, Massachusetts Institute of Technology
Talk Title: The Trajectory of Metal Additive Manufacturing
Abstract: Manufacturing of metal components is essential to every major industry, and involves complex supply chains, consumes significant natural resources, and sometimes still uses ancient techniques. Conversely, additive manufacturing (AM) promises to, ultimately, digitize the shaping of components and enable distributed production. I will highlight recent work from my research group at MIT and collaborators on metal AM including discrete element simulation of powder spreading coupled with X-ray microscopy for layer quality control; a new concept for drop-on-demand metal printing; and physics-based cost modeling to guide the deployment of AM at scale. I will also discuss our efforts in manufacturing education and workforce training.
Biography: John Hart is Professor of Mechanical Engineering, Director of the Center for Additive and Digital Advanced Production Technologies, and Director of the Laboratory for Manufacturing and Productivity, at MIT. His research group at MIT, the Mechanosynthesis Group focuses on science and technology of production, including research in additive manufacturing, nanostructured materials, and precision machine design. In 2017 and 2018, respectively, he received the MIT Ruth and Joel Spira Award for Distinguished Teaching in Mechanical Engineering and the MIT Keenan Award for Innovation in Undergraduate Education. He is a co-founder of Desktop Metal and VulcanForms, and a Board Member of Carpenter Technology Corporation.
Host: Center for Advanced Manufacturing
More Info: https://usc.zoom.us/webinar/register/WN_lp3nfkY6TQ6brG0kB-c2Ag
Webcast: https://usc.zoom.us/webinar/register/WN_lp3nfkY6TQ6brG0kB-c2AgWebCast Link: https://usc.zoom.us/webinar/register/WN_lp3nfkY6TQ6brG0kB-c2Ag
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/webinar/register/WN_lp3nfkY6TQ6brG0kB-c2Ag
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CS Colloquium: Priya Donti (Carnegie Mellon University) - Optimization-in-the-loop AI for energy and climate
Fri, Apr 08, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Priya Donti , Carnegie Mellon University
Talk Title: Optimization-in-the-loop AI for energy and climate
Series: CS Colloquium
Abstract: Addressing climate change will require concerted action across society, including the development of innovative technologies. While methods from artificial intelligence (AI) and machine learning (ML) have the potential to play an important role, these methods often struggle to contend with the physics, hard constraints, and complex decision-making processes that are inherent to many climate and energy problems. To address these limitations, I present the framework of "optimization-in-the-loop AI," and show how it can enable the design of AI models that explicitly capture relevant constraints and decision-making processes. For instance, this framework can be used to design learning-based controllers that provably enforce the stability criteria or operational constraints associated with the systems in which they operate. It can also enable the design of task-based learning procedures that are cognizant of the downstream decision-making processes for which a model's outputs will be used. By significantly improving performance and preventing critical failures, such techniques can unlock the potential of AI and ML for operating low-carbon power grids, improving energy efficiency in buildings, and addressing other high-impact problems of relevance to climate action.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Priya Donti is a Ph.D. Candidate in Computer Science and Public Policy at Carnegie Mellon University. Her research explores methods to incorporate physics and hard constraints into deep learning models, in order to enable their use for forecasting, optimization, and control in high-renewables power grids. She is also a co-founder and chair of Climate Change AI, an initiative to catalyze impactful work in climate change and machine learning. Priya is a recipient of the MIT Technology Review's 2021 "35 Innovators Under 35" award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.
Host: Bistra Dilkina
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: By invitation only.
Contact: Assistant to CS chair
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PhD Defense - Eric Heiden
Fri, Apr 08, 2022 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Eric Heiden
Time: April 8, 11am-1pm PT
Location: RTH 406 and on Zoom (https://usc.zoom.us/j/9965174023?pwd=SzlUV1NSUlZQVUNGZTNlT2h4YWpjQT09)
Committee:
Gaurav Sukhatme (chair), Jernej Barbic, S.K. Gupta, Sven Koenig, Stefanos Nikolaidis
Title: Closing the Reality Gap via Simulation-based Inference and Control
Abstract:
Simulators play a crucial role in robotics - serving as training platforms for reinforcement learning agents, informing hardware design decisions, or facilitating the prototyping of new perception and control pipelines, among many other applications. While their predictive power offers generalizability and accuracy, a core challenge lies in the mismatch between the simulated and the real world. This thesis addresses the reality gap in robotics simulators from three angles.
First, through the lens of robotic control, we investigate a robot learning pipeline that transfers skills acquired in simulation to the real world by composing task embeddings, offering a solution orthogonal to commonly used transfer learning approaches. Further, we develop an adaptive model-predictive controller that leverages a differentiable physics engine as a world representation that is updatable from sensor measurements.
Next, we develop two differentiable simulators that tackle particular problems in robotic perception and manipulation. To improve the accuracy of LiDAR sensing modules, we build a physically-based model that accounts for the measurement process in continuous-wave LiDAR sensors and the interaction of laser light with various surface materials. In robotic cutting, we introduce a differentiable simulator for the slicing of deformable objects, enabling applications in system identification and trajectory optimization.
Finally, we explore techniques that extend the capabilities of simulators to enable their construction and synchronization from real-world sensor measurements. To this end, we present a Bayesian inference algorithm that finds accurate simulation parameter distributions from trajectory-based observations. Next, we introduce a hybrid simulation approach that augments an analytical physics engine by neural networks to enable the learning of dynamical effects unaccounted for in a rigid-body simulator. In closing, we present an inference pipeline that finds the topology of articulated mechanisms from a depth or RGB video while estimating the dynamical parameters, yielding a comprehensive, interactive simulation of the real system.
WebCast Link: https://usc.zoom.us/j/9965174023?pwd=SzlUV1NSUlZQVUNGZTNlT2h4YWpjQT09
Audiences: Everyone Is Invited
Contact: Computer Science Department
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DEN@Viterbi - 'Limited Status: How to Get Started' Virtual Info Session
Fri, Apr 08, 2022 @ 12:00 PM - 01:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi for our upcoming Limited Status: How to Get Started Virtual Information Session via WebEx to learn about the Limited Status enrollment option. The Limited Status enrollment option allows individuals with an undergraduate degree in engineering or related field, with a 3.0 GPA or above to take courses before applying for formal admission into a Viterbi graduate degree program.
USC Viterbi representatives will provide a step-by-step guide for how to get started as a Limited Status student and enroll in courses online via DEN@Viterbi as early as the Spring 2023 semester.
Register Now!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=e76b82fb5ee7da672f602f537a091ce38
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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CILQ Internal Seminar
Fri, Apr 08, 2022 @ 12:00 PM - 01:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Keith Chugg, Professor, USC
Talk Title: Co-Design of Algorithms and Hardware for Deep Neural Networks
Abstract: Neural networks are in wide use in cloud computing platforms. This includes inference and training with the latter typically performed on programmable processors with multiply-accumulate (MAC) accelerator arrays (e.g., GPUs). In many applications, it can be describable to train on an edge device or using energy efficient application specific circuits. In this talk I will present some research results on application specific hardware acceleration methods for neural networks. Pre-defined sparsity is a method to reduce the complexity of training and inference. In contrast to pruning approaches which remove edges/weights during or after training, this approach sets a pre-defined pattern of sparse connection prior to training and holds this pattern fixed during training and inference. This allows one to design the pattern of sparsity to match a specific hardware acceleration architecture. We also consider Logarithmic Number Systems (LNS) for implementation of training. With LNS, operations are performed on the log of the quantities and therefore multiplies are simplified to addition while additions are more complex in the log domain. We present some preliminary results for LNS training and highlight ongoing challenges in applying this to larger, more complex networks. In many of these approaches we borrow from the design and implementation of iterative decoders for digital communication systems.
Host: CILQ
Webcast: https://usc.zoom.us/j/92417517950?pwd=WUkycy90cndVQko5R3RhQ1U3STBDdz09More Information: ChuggSeminar-Apr8-2022.pdf
Location: via zoom
WebCast Link: https://usc.zoom.us/j/92417517950?pwd=WUkycy90cndVQko5R3RhQ1U3STBDdz09
Audiences: Everyone Is Invited
Contact: Corine Wong
