Vickie Winston

Friday, May 12, 2023
11:00 a.m. – 12:00 p.m. (CST)
Fishbowl (WEB 333), In-Person Presentation Only

Aditya Arun, PhD Student
Center of Wireless Communications (CWC) and Contextual Robotics Institute (CRI)
University of California, San Diego 

Title: “Leveraging WiFi for Robust and Resource-Efficient SLAM”

Talking Points: 

• Simultaneous localization and mapping (SLAM) issues such as errors in odometry or visual sensor measurements, and “Loop closures”
• Perceptual aliasing, loop closure failures, and deployments on small form-factor hardware
• Incorporation of  Wi-Fi sensors within existing SLAM systems

Abstract
Indoor robots can increasingly deliver value in diverse industry segments, including logistics, security, and construction. This demand has consequently increased the importance of robust simultaneous localization and mapping (SLAM) algorithms for indoor robots. This robustness is typically provided by fusing information from visual sensors (LiDARs or cameras) with proprioceptive sensors (odometers or IMUs). However, visual sensors can be sensitive to perceptual aliasing, visually dynamic environments, and changing lighting conditions, resulting in failures in SLAM predictions.

In this talk, I will present WiFi radios as camera-like sensors capable of circumventing these issues and develop a real-time SLAM system to provide drift-free trajectory updates. We build our system with off-the-shelf components and evaluate it over four large-scale datasets in three indoor environments, traveling a cumulative distance of over 1500 m. Through these extensive evaluations, we find employing WiFi-based sensing provides a 6x improvement over purely relying on odometry. Additionally, we see a 4x reduction in compute and memory consumption compared to state-of-the-art Visual and Lidar SLAM systems.

Biography
Aditya Arun is a fourth-year Ph.D. student at the University of California, San Diego, advised by Dinesh Bharadia. He is part of the WCSNG group, the Center for Wireless Communications (CWC), and the Contextual Robotics Institute (CRI). His larger research vision is to incorporate WiFi and other wireless technologies as sensing modalities to improve the world of robotics and enable robotics to solve real-world problems. His research interests span wireless sensing, robotics, signal processing, and networking. Previously, he completed his B.S. from the University of California, Berkeley.

More on Aditya Arun: http://wcsng.ucsd.edu/aarun/

More on CESG Seminars: HERE

Please join on Friday, 5/12/23 at 11:00 a.m. in the Fishbowl (WEB 333).

Friday, 4/14/23:  2:00 p.m. – 4:00 p.m.

Located just inside ZACH from the E-Quad in the Virginia Brown Atrium on the right side

Link to list of topics, titles, and presenters: https://cesg.tamu.edu/poster_2023/

You will find 26 MS and PhD Research Posters on a variety of topics related to Computer Systems, Computer Architecture, Communication Networks, CAD, VLSI, AI, Security, and Machine Learning.

Come by to:

• Check out and support our CE MS and PhD researchers.
• Learn about work they are doing, expand your knowledge, and ask questions.
• Vote on-site for your favorite poster presentations.
• Pick up some flyers, swag, and pizza as you walk around!

Friday, March 31, 2023
3:50 – 4:50 p.m. (CST)
Zoom (see syllabus or email list for link)

Archana Bura 
PhD Candidate, Spring 2023
Dept. of Electrical and Computer Engineering
Texas A&M University

Title: “Constrained Reinforcement Learning for Wireless Networks ”

Talking Points

• Challenges in learning under real world problems with constraints
• Developing CRL algorithms for two real world resource allocation problems under constraints
• Safe exploration in learning a generic Constrained MDP

Abstract
In this talk, I will discuss how we efficiently applied reinforcement learning methods for real world problems. I consider two motivating real world problems: Resource allocation for Media streaming at the Wireless edge, and Resource block allocation in an Open RAN system. Under throughput, latency and resource constraints, these systems can be modeled by Constrained Markov Decision Processes (CMDPs). Since these systems have complex dynamics, a constrained reinforcement learning (CRL) approach is attractive for determining an optimal control policy. Applying off-the-shelf RL algorithms yields better results compared to naive solutions, but these algorithms need a lot of samples to train or have high complexity. We overcome these issues by providing CRL methods that efficiently utilize the structure in the problem. Motivated by these results, we study the fundamental “safe exploration” problem in a generic CRL, and propose a safe RL method that does not violate constraints during the learning process with high probability.

Biography
Archana Bura is a PhD candidate at Texas A&M University’s Department of Electrical and Computer Engineering, where she specializes in constrained reinforcement learning. Her research centers around implementing RL for real world problems, where safety is critical, by developing algorithms and theory of constrained reinforcement learning that leverage structure to enhance the learning process.

More on Archana Bura: HERE

More on CESG Seminars: HERE

Please join on Friday, 3/31/22 at 3:50 p.m. via Zoom.
Zoom option: Links and PW in syllabus or found in email announcement.

Friday, April 21, 2023
3:50 – 4:50 p.m. (CST)
ZOOM

Peipei Zhou
Assistant Professor
Dept. Electrical and Computer Engineering
University of Pittsburgh

Title: “CHARM: Composing Heterogeneous AcceleRators for Matrix Multiply on Versal ACAP Architecture”

Talking Points

• Which platform beats 7nm GPU A100 in energy efficiency? AMD Versal ACAP (FPGA+AI Chip)!
• How to program AMD Versal ACAP, i.e., FPGA + AI Chip within the same chip die for deep learning applications in 10 lines of code? Use CHARM!

Abstract
Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogeneous architectures featuring both FPGA and dedicated ASIC accelerators have emerged as promising platforms. For example, the AMD/Xilinx Versal ACAP architecture combines general-purpose CPU cores and programmable logic (PL) with AI Engine processors (AIE) optimized for AI/ML. An array of 400 AI Engine processors executing at 1 GHz can theoretically provide up to 6.4 TFLOPs performance for 32-bit floating-point (fp32) data. However, machine learning models often contain both large and small MM operations. While large MM operations can be parallelized efficiently across many cores, small MM operations typically cannot. In our investigation, we observe that executing some small MM layers from the BERT natural language processing model on a large, monolithic MM accelerator in Versal ACAP achieved less than 5% of the theoretical peak performance. Therefore, one key question arises: How can we design accelerators to fully use the abundant computation resources under limited communication bandwidth for end-to-end applications with multiple MM layers of diverse sizes? In this talk, we will discuss CHARM framework to compose multiple diverse MM accelerator architectures working concurrently towards different layers within one application. CHARM includes analytical models which guide design space exploration to determine accelerator partitions and layer scheduling. To facilitate the system designs, CHARM automatically generates code, enabling thorough onboard design verification. We deploy the CHARM framework for four different deep learning applications, including BERT, ViT, NCF, and MLP, on the AMD/Xilinx Versal ACAP VCK190 evaluation board. Our experiments show that we achieve 1.46 TFLOPs, 1.61 TFLOPs, 1.74 TFLOPs, and 2.94 TFLOPs inference throughput for BERT, ViT, NCF, MLP, respectively, which obtain 5.40x, 32.51x, 1.00x and 1.00x throughput gains compared to one monolithic accelerator.

Biography
Peipei Zhou is an assistant professor of the Electrical Computer Engineering (ECE) department at the University of Pittsburgh. She has over 10 years of experience in hardware and software co-design. She has published 20+ papers in top-tier IEEE/ACM computer system and design automation conferences and journals including FPGA, FCCM, DAC, ICCAD, ISPASS, TCAD, TECS, TODAES, IEEE Micro, etc. The algorithm and tool proposed in her FCCM’18 paper have been realized in the commercial Vitis HLS (high-level synthesis) compiler from Xilinx (acquired by AMD in Feb 2022). Her work in FPGA acceleration for deep learning won the 2019 Donald O. Pederson Best Paper Award from the IEEE Council for Design Automation (CEDA). Her work in cloud-based application optimization won the 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) Best Paper Nominee and her work in FPGA acceleration for computer vision won the 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) Best Paper Nominee. Before joining Pitt, she worked as a full-time staff software engineer in a start-up company and led a team of 6 members to develop CNN and MM kernels in the deep learning libraries for two generations of AI training application-specific integrated circuit (ASIC) chip products.

More on Dr. Zhou:
Homepage: https://peipeizhou-eecs.github.io/
Google Scholar: https://scholar.google.com/citations?user=px_jwFgAAAAJ&hl=en

More on CESG Seminars: HERE

Please join on Friday, 4/21/22 at via Zoom (see emails or syllabus for link and password)

Friday, March 24, 2023
3:50 – 4:50 p.m. (CST)
Zoom (see syllabus or email list for link)

Desik Rengarajan 
PhD Candidate, Spring 2023
Dept. of Electrical and Computer Engineering
Texas A&M University

Title: “Enhancing Reinforcement Learning Using Data and Structure”

Talking Points

• Challenges in learning in sparse reward environments
• Developing RL algorithms that take advantage of sub-optimal demonstration data to learn in sparse reward environments
• Developing meta-RL algorithms that take advantage of sub-optimal demonstration data and structure to learn in sparse reward environments

Abstract
In reinforcement learning, reward functions serve as an indirect method of defining the goal of the algorithm. Designing a reward function that accurately captures the task at hand while effectively guiding the learning process can be a difficult challenge, requiring expert domain knowledge and manual fine-tuning. To overcome this, it is often easier to rely on sparse rewards that merely indicate partial or complete task completion. However, this leads to RL algorithms failing to learn an optimal policy in a timely manner due to the lack of fine-grained feedback. During this talk, I will delve into the impact of sparse rewards on reinforcement and meta reinforcement learning and present algorithms that leverage sub-optimal demonstration data to overcome these challenges.

Biography
Desik Rengarajan is a PhD candidate at Texas A&M University’s Department of Electrical and Computer Engineering, where he specializes in reinforcement learning. His research centers on the development of reinforcement learning algorithms that take advantage of side information, such as demonstration data and structure, to enhance the learning process and overcome challenges that arise when implementing RL in real-world situations.

More on Desik Rengarajan: HERE

More on CESG Seminars: HERE

Please join on Friday, 3/24/22 at 3:50 p.m. via Zoom.
Zoom option: Links and PW in syllabus or email announcement.

Friday, February 24, 2023
3:50 – 4:50 p.m. (CST)
ETB 1020 

Dr. Manoranjan Majji
Associate Professor
Dept. of Aerospace Engineering
Texas A&M University

Title: “Advances in Computer Engineering: Impact on Aerospace Applications ”

Talking Points

• Revolutions in computing continue to advance a wide variety of aerospace vehicle navigation and control problems. Three broad applications are discussed to demonstrate this tangible impact.
• Recent research advances in space manufacturing and assembly automation at LASR lab.
• Novel velocimeter LIDAR and interferometric rate sensing technologies developed by Prof. Majji and his students are discussed.
• New embedded processing pipelines developed by Prof. Majji’s students to estimate the forces sensed by optomechanical accelerometers developed by Prof. Guzman are elaborated.

Abstract

Recent advances in aerospace vehicle guidance, navigation and control furthered by emerging computer engineering technologies are elaborated in the lecture. Novel approaches for relative navigation using doppler sensing technologies are outlined with applications to terrain relative navigation and ship landing. Approaches to automate space systems and manufacture elements of swarm satellites in space are demonstrated using proximity operation emulation robots developed at the Land, Air and Space Robotics (LASR) laboratory. Embedded compute elements to process sensor data in order to realize an advanced optomechanical accelerometer are described to showcase advances in space avionics. The new accelerometer technology developed in collaboration with Prof. Felipe Guzman is discussed, which is found to enable spacecraft autonomy.

Biography
Dr. Manoranjan Majji is an Associate Professor of Aerospace Engineering and is the Director of the Land, Air and Space Robotics (LASR) Laboratory at Texas A&M University. He has a diverse background in several aspects of dynamics and control of aerospace vehicles with expertise spanning the whole spectrum of analysis, modeling, computations and experiments. In the areas of astrodynamics, estimation and system identification, he has made fundamental contributions documented in over 170 publications (including 45 journal articles) in the areas of guidance, navigation and control. Working with a team of 20 graduate students and 6 undergraduate researchers at the LASR lab, he works on a variety of research projects sponsored by NGA, NASA, JPL, AFRL, AFOSR, ONR, DARPA, JHTO, and the IC, in addition to various industrial partners, including BlackSky Geospatial, Dezyne Technologies, and VectorNav Technologies. His 10 PhD graduates are making valuable contributions in the academic, national laboratory, and industrial research establishments. In addition to being a scholar, Majji has a great deal of engineering experience developing software systems and embedded systems from OEM products. He holds a provisional patent on a simultaneous location and mapping software suite and was awarded a patent for developing a novel omni directional robot. He has disclosed various sensor inventions in the past decade. Manoranjan is the recipient of the 2021 Dean of Engineering Excellence Award at Texas A&M and the 2021 Texas A&M Institute of Data Science Career Initiation Fellowship. He is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA), a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and is a Fellow of the American Astronautical Society (AAS).

More at https://cesg.tamu.edu/people-2/faculty/jiang-hu/

More on CESG Seminars: HERE

Please join on Friday, 2/24/22 at 3:50 p.m. in ETB 1020.
Zoom option: Links and PW in syllabus or email announcement.

Friday, February 10, 2023
3:50 – 4:50 p.m. (CST)
ETB 1020 

Dr. Jiang Hu
Professor
Dept. of Electrical and Computer Engineering
Affiliate of Computer Science Electrical and Computer Engineering
Texas A&M University

Title: “Machine Learning for EDA and EDA for Machine Learning”

Talking Points

• A stochastic approach to handling noisy labels in machine learning models for chip design automation
• An analytical approach to co-optimization of CNN hardware and dataflow mapping

Abstract
The wave of machine learning splashes to almost every corner of the world due to its unprecedented success. The first part of this talk will be focused on how to leverage machine learning for EDA (Electronic Design Automation). Specifically, a machine learning-based early routability prediction technique will be introduced. This technique provides a stochastic approach to handling non-deterministic data labels, which may exist in other machine learning applications. In the second part, an EDA technique for ML hardware acceleration will be presented. This is the first analytical approach to CNN hardware and dataflow co-optimization, and outperforms state-of-the-art methods in terms of both solution quality and computation runtime.

Biography
Dr. Jiang Hu is a professor in the Department of Electrical and Computer Engineering at Texas A&M University. His research interests include design automation of VLSI circuits and systems, computer architecture optimization and hardware security. He has published over 240 technical papers. He received best paper awards at DAC 2001, ICCAD 2011, MICRO 2021 and ASPDAC 2023. He was the technical program chair and the general chair of the ACM International Symposium on Physical Design (ISPD) in 2011 and 2012, respectively. He was named an IEEE fellow in 2016. He will serve as the program co-chair for ACM/IEEE Workshop on Machine Learning for CAD 2023.

More at https://cesg.tamu.edu/people-2/faculty/jiang-hu/

More on CESG Seminars: HERE

Please join on Friday, 2/10/22 at 4:10 p.m. in ETB 1020.
Zoom option: Links and PW in syllabus or email announcement.