Northeastern University College of Engineering

Graduate Student Fellowship Writing Club-Hosted by NU CommLab and NetSI
2024-07-24 15:00 - 16:00, 2024-07-31 15:00 - 16:00, 2024-08-07 15:00 - 16:00 (America/New_York)
Join the NU CommLab and NetSI sponsored weekly graduate student fellowship writing club for support in writing your fellowship application! The fellowship writing club meets virtually on Wednesdays from 3-4pm from July 10- August 21. We will offer you an opportunity to ask questions to faculty, staff and students who have reviewed, mentored or applied and received fellowships. We will provide fellowship writing tips and guidance as well as offer writing and draft review sessions. Register to join our Zoom Sessions.

LinkedIn, CV, Resume: CommLab Drop-In Workshops
2024-07-24 16:00 - 17:00, 2024-07-31 16:00 - 17:00
Join the CommLab’s empowering LinkedIn, CV, and Resume Drop-In Workshops any Wednesday from 4 pm to 5 pm ET. This collaborative space offers valuable advice and peer feedback to enhance your online profile and professional presence. Join this workshop series through Zoom.

Rui Lou PhD Dissertation Defense
2024-07-25 14:00 - 15:30
Announcing:
PhD Dissertation Defense
Name:
Rui Luo
Title:
Shared Assistance Methods for Human-in-the-loop Robot Systems
Date:
7/25/2024
Time:
2:00:00 PM
Location:
EXP 701A.
Committee Members:
Prof. Taskin Padir (Advisor)
Prof. John Peter Whitney
Prof. Yanzhi Wang
Dr. Mark Zolotas
Abstract:
Fully autonomous robot systems, though highly desired, face substantial theoretical and practical challenges when being deployed into a dynamic environment where human co-exists. To tackle this challenge, this thesis investigates the concept of human-in-the-loop (HITL) systems, which incorporate human input to enhance robot functionality. HITL systems offer a pragmatic alternative, combining human versatility with robotic precision.
This research aims to address critical questions in one specific HITL system which prioritizes the dominant role of human within the system, positioning the robot primarily in an assistive capacity that adheres to human commands to facilitate the achievement of a shared goal. It explores two primary paradigms of shared assistance methods—Shared Control (SC) and Shared Autonomy (SA)—and discuss the system designs as well as specific algorithms to implement the three critical components in a HITL systems: human intention estimation, modulation of human inputs and robot autonomy, and the human-robot communication channel.
Due to the variety of use cases and their specific challenges, four distinct HITL systems are developed and analyzed to exemplify how shared assistance methods could be incorporated to assist human operators: an assistive wheelchair for indoor navigation, a human-centered robot system design for industrial tasks, a mobile bi-manual robot for tele-manipulation, and a VR-based customizable shared control system for fine teleopeartion. Although each system represents a comprehensive robotic solution, the research contributions for each work vary.
In the assistive wheelchair navigation system, the focus was on human intent estimation via low-throughput interface utilizing a recursive Bayesian filter, with significant efforts dedicated to developing a real-time user interface serving as the communication channel. In the human-robot collaboration system for industrial setting, the emphasis was on human state estimation through camera-based posture tracking and exploring the interplay between robot behavior and human ergonomics. For the two teleoperation systems, the primary focus was on the real-time modulation of human inputs and robot autonomy to aid in achieving dexterous manipulation tasks. A novel VR-based user interface was developed to enable users to customize the level of robotic autonomous assistance. Each system was validated through a pilot study involving 10-20 human subjects, accompanied by extensive data analysis to provide insights into designing HITL systems for various applications.
In conclusion, this thesis contributes to a deeper understanding of HITL systems, highlighting their potential to enhance human productivity, ergonomics, and quality of life in various applications through concrete examples. The integration of human intent estimation and real-time shared control methods into robotic systems demonstrates the feasibility and benefits of HITL approaches. Our extensive experimental analysis underscores the critical role of human feedback in designing practical HITL systems that can be deployed in real-world scenarios.

Mock Interview: CommLab Drop-In Workshops
2024-07-25 16:00 - 17:00, 2024-08-01 16:00 - 17:00, 2024-08-08 16:00 - 17:00
Join the CommLab any Thursday from 4-5pm ET, we’ll delve into the intricacies of interviews, unveiling effective preparation strategies for any interview scenario. Engage in an interactive setting as we dissect the overall interview experience, discuss common interview scenarios, and share insights on what to do during critical moments. Join this hybrid workshop series through Zoom.

Poster Design and Presentation: CommLab Drop-In Workshops
2024-07-25 17:00 - 18:00, 2024-08-01 17:00 - 18:00, 2024-08-08 17:00 - 18:00
The CommLab will host drop-in workshops for poster design and presentation to focus on crafting the best visual communication of your research and telling your research story! We will discuss techniques and implement communication strategies to successfully showcase your work. No matter where you are in the process, whether it is just in the idea phase or you are trying to polish your final poster, we are happy to help you. Join us any Thursday from 5-6pm, on Zoom.

Graduate School of Engineering Campus Tour - In Person
2024-07-27 13:00 - 14:00, 2024-08-03 13:00 - 14:00
Want to learn more about Northeastern’s Boston campus? Then we welcome you to sign up for a Graduate School of Engineering campus tour! Led by one of our expert Graduate Student Ambassadors, we’ll show you key locations on campus, in addition to resources specific to Engineering, and answer your questions about Boston. Please complete the registration form linked below to select the date and time that works best for you. Tours are open to all students interested in learning more about the Graduate School of Engineering. We can’t wait to meet you!

Ruyi Ding PhD Proposal Review
2024-07-29 09:00 - 10:30
Name:
Ruyi Ding
Title:
Towards Robust and Secure Deep Learning: From Training through Deployment to Inference
Date:
7/29/2024
Time:
9:00:00 AM
Committee Members:
Prof. Yunsi Fei (Advisor)
Prof. Aidong Ding
Prof. Lili Su
Abstract:
In recent years, deep learning has experienced rapid advancement, leading to the development of numerous commercial deep neural network (DNN) models across diverse fields such as autonomous driving, healthcare, and recommendation systems. However, this wide adoption has intensified concerns about AI security throughout a neural network’s lifecycle — from training to deployment, and inference. Various vulnerabilities have emerged, threatening confidentiality, privacy, and intellectual property (IP) rights: poisoned training datasets facilitate privacy leakage and backdoor injection; after deployment, models may be misused through unauthorized transfer learning, a new form of IP infringement, and weights and parameters are subject to side-channel assisted model extraction attacks; during inference, adversarial attacks may compromise DNN functionality, causing misclassifications.
This dissertation addresses new security challenges across the neural network lifecycle through several novel contributions. We identify a new poisoning vulnerability in graph neural networks, where injecting poisoned nodes exacerbates link privacy leakage, allowing attackers to steal adjacent information from private training data, highlighting the necessity of robust AI training. To prevent model misuse after deployment, we introduce EncoderLock and Non-transferable Pruning, employing innovative training schemes and pruning methods to restrict the malicious use of pre-trained models through transfer learning, effectively implementing applicability authorization. Towards secure deep learning implementations, we adopt a software-hardware co-design approach to address DNN vulnerabilities. Specifically, we leverage the electromagnetic emanations from DNN accelerators in a new approach called EMShepherd, which detects adversarial examples (AE) on edge devices in a ‘black-box’ manner. To protect deployed DNNs against side-channel-based weight-stealing attacks, we develop PixelMask, which leverages the characteristics of DNN for side-channel defense by masking out unimportant inputs and dropping related operations to obfuscate side-channel signals. Lastly, we explore the use of Trusted Execution Environments (TEE) to safeguard model weights and data privacy against model stealing and membership inference attacks.
This proposal identifies key challenges of robust and secure deep learning, tackles vulnerabilities at various stages of the AI lifecycle, and provides comprehensive protection mechanisms, from securing the training process to safeguarding deployed models, paving the way for more resilient and reliable AI technologies in real-world applications.

CommLab Drop-In Writing Hours
2024-07-29 10:00 - 12:00
Graduate students, are you looking for a place for focused research writing time? Join the CommLab drop-in writing hours any Mondays from 10 am-12 pm ET. Drop in any Monday and stay for a short time or the whole two hours. CommLab Fellows will be available to provide feedback on your writing. We will be meeting in 13 International Village.

Shijie Yan PhD Proposal Review
2024-07-29 12:00 - 13:30
Name:
Shijie Yan
Title:
Efficient Monte Carlo light transport algorithms in complex scattering media
Date:
7/29/2024
Time:
12:00:00 PM
Committee Members:
Prof. Qianqian Fang (Advisor)
Prof. Steven Jacques
Prof. David Kaeli
Prof. Edwin Marengo
Abstract:
Modeling light-tissue interactions is crucial for many optical imaging modalities, for which the Monte Carlo (MC) method has been widely recognized as the gold-standard. Despite dramatic speed improvements gained via the use of graphics processing units (GPUs), MC simulations remain computationally intensive. Efficient and accurate MC algorithms are needed to further consider physiologically realistic tissue models, especially for emerging optical imaging techniques. Voxel-based MC (VMC) and mesh-based MC (MMC) are two major MC methods for modeling complex tissues with their respective strengths and weaknesses. While VMC offers higher computational efficiency due to the simple data structure, its accuracy suffers from the terraced boundary shape especially in low-scattering medium; on the other side, MMC offers improved boundary fidelity but can be slow and memory-intensive, particularly at high mesh density. Furthermore, emerging wide-field diffuse optical imaging systems using structured light require more efficient modeling to handle numerous illumination patterns. Additionally, niche applications such as polarized light imaging could also benefit from many of the recent advances from modern MC simulations such as GPU acceleration and handling of complex heterogeneous media.
This proposal is aimed to push the frontiers of modern MC simulation algorithms to fundamentally enhance their utilities in diverse applications. To reduce the staircase effect in VMC, we have developed a hybrid MC algorithm, named split-voxel MC (SVMC), where sub-voxel oblique surfaces are extracted using a marching-cubes algorithm and are incorporated into a memory-efficient voxelated data structure. SVMC allows VMC to handle curved surfaces while remaining computationally efficient. A GPU-accelerated marching-cubes algorithm was also developed to further accelerate SVMC domain preprocessing. On the other hand, to further improve MMC computational efficiency, a dual-grid MMC (DMMC) algorithm was developed to perform fast ray-tracing inside a coarse tetrahedral mesh while saving fluence data over a dense voxelated grid, simultaneously achieving improved speed and output accuracy. To accommodate increasing needs of modeling wide-field pattern based sources, we have developed a “photon sharing’’ MC algorithm that performs simulations of all illumination and detection patterns in parallel, improving computational speed by an order of magnitude. Additionally, we have developed a GPU-accelerated massively-parallel algorithm capable of modeling Mie scattering of sphere particles in three-dimensional media for polarized light imaging, achieving nearly 1000$times$ speed acceleration compared to sequential implementation.
Lastly, we have also investigated a hardware-accelerated MMC algorithm using the NVIDIA OptiX ray-tracing framework, leveraging modern GPU ray-tracing (RT) cores extensively optimized for graphics rendering. Preliminary results demonstrate comparable accuracy and significantly improved simulation speed compared to conventional tetrahedral MMC.

Yunus Bicer PhD Dissertation Defense
2024-07-29 15:00 - 17:00
Name:
Yunus Bicer
Title:
Novel Methods for Electromyographic Hand Gesture Recognition: Expressive Gestures Sets with Minimal Calibration
Date:
7/29/2024
Time:
3:00:00 PM
Location:
ISEC 632 –
Committee Members:
Prof. Deniz Erdogmus (Advisor)
Prof. Mathew Yarossi (Co-Advisor)
Prof. Eugene Tunik
Prof. Tales Imbiriba
Abstract:
Gesture recognition, the process of interpreting hand gestures through computational algorithms and devices, is essenatial for enhancing human-computer interaction(HCI). This thesis focuses on surface electromyography (sEMG)-based gesture recognition, where the signals generated by muscles are analyzed to identify hand gestures. sEMG systems provides more natural and intuitive interactions compared to traditional input methods and hold significant potential in assistive technology, prosthetics, and immersive environments such as virtual and augmented reality. Despite these advantages, sEMG-based methods face challenges including user-specific variability in signals, limited gesture expressivity, and the need for extensive calibration time. This research aims to address these issues by proposing novel methods for minimizing calibration time and expanding expressivity of gesture recognition capabilities. Key innovations include a real-time probability feedback mechanism to facilitate user adaptation and techniques to recognize a wider range of gestures with minimal training data. This work seeks to enhance the usability and versatility of sEMG-based systems, making them more accessible and effective for various applications.

Kyle Lockwood PhD Dissertation Defense
2024-07-30 13:30 - 14:30
Name:
Kyle Lockwood
Title:
Leveraging Submovements for Prediction and Trajectory Planning in Human-Robot Handover
Date:
7/30/2024
Time:
1:30:00 PM
Location:
ISEC 532 –
Committee Members:
Prof. Deniz Erdogmus (Advisor)
Prof. Eugene Tunik (Co-Advisor)
Prof. Mathew Yarossi
Prof. Tales Imbiriba
Abstract:
Collaborative physical interactions between humans and robots pose difficult modeling challenges. To create natural interactions, engineers must consider human inference of intent, anticipation of action, and coordination of movement. Humans can handle these challenges effortlessly when interacting with one another, but they are very difficult to overcome in robot implementations. Although human-human handover is a seemingly simple task, it requires a complex perception-action coupling to determine when and where the handover will happen, as well as choosing an appropriate trajectory to receive the object. Critically, modeling human-robot handover requires incorporating knowledge about human inference and trajectory planning to obtain seamless interactions. Despite recent advancements in sensing and control, human-robot handovers are far from approaching the fluidity and flexibility of human-human collaboration. Existing predictive models applied to human-robot handover often utilize classification methods and other approaches that suffer in accuracy when encountering noisy human trajectories that are not captured during their training. To address these challenges, this work presents two models that act as robotic surrogates for human inference and trajectory planning in a handover task. This approach delivers promising results while remaining grounded in a physiologically meaningful feature of human motion: Gaussian-shaped submovements in velocity profiles. This thesis analyzes human-human handover kinematics to establish a baseline for model evaluation and investigate the influence of handover role, it presents models for human inference and trajectory planning, and it applies the inference model in human-robot handover experiments.

LeetCode Mock Interviews – CommLab Drop-In Workshops
2024-07-30 16:00 - 17:00, 2024-08-06 16:00 - 17:00
Join the CommLab any Tuesday from 4-5 PM for our weekly LeetCode Mock Interview Workshop via Zoom. This workshop is tailored towards programming jobs and prior coding knowledge is expected. Boost your LeetCode problem-solving confidence for interviews by building your speaking skills while solving programming problems.

CommLab Drop-In Writing Hours
2024-08-02 11:00 - 12:30 | 13 International Village, 360 Huntington Ave, 13 INV, Boston, MA, 02115, United States
Graduate students, are you looking for a place for focused research writing time? Join the CommLab drop-in writing hours any Friday from 11 am-12:30 pm ET. Drop in any Friday and stay for a short time or the whole hour and a half. CommLab Fellows will be available to provide feedback on your writing. We will be meeting in 13 International Village.

Joshua Groen PhD Proposal Review
2024-08-05 14:00 - 15:00
Name:
Joshua Groen

Title:
Optimizing and Securing Open RAN with Experimental System Validation
Date:
8/5/2024
Time:
2:00:00 PM
Location:
ISEC232;
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof Engin Kirda
Dr. Christopher Morrell
Abstract:
5G and beyond cellular networks promise remarkable advancements in bandwidth, latency, and connectivity, with the emergence of Open Radio Access Network (Open RAN) representing a pivotal direction. O-RAN inherently supports machine learning (ML) for network operation control, with RAN Intelligence Controllers (RICs) utilizing ML models developed by third-party vendors based on key performance indicators (KPIs) from geographically dispersed base stations or user equipment (UE). Realistic and robust datasets are crucial for developing these ML models. We collect a comprehensive 5G dataset using real-world cell phones across diverse scenarios and replicate this traffic within a full-stack srsRAN-based O-RAN framework on Colosseum, the world’s largest radio frequency (RF) emulator. This process produces a robust, O-RAN compliant KPI dataset reflecting real-world conditions, enabling the training of ML models for traffic slice classification with high accuracy.
The O-RAN paradigm introduces cloud-based, multi-vendor, open, and intelligent architectures, enhancing network observability and reconfigurability. However, this also expands the threat surface, exposing components and ML infrastructure to cyberattacks. We examine O-RAN security, focusing on specifications, architectures, and intelligence proposed by the O-RAN Alliance. We identify threats, propose solutions, and experimentally demonstrate their effectiveness in defending O-RAN systems against cyberattacks, offering a holistic and practical perspective on O-RAN security.
We investigate the impact of encryption on two key O-RAN interfaces: the E2 interface and the Open Fronthaul, using a full-stack O-RAN ALLIANCE compliant implementation within the Colosseum network emulator and a production-ready Open RAN and 5G-compliant private cellular network. Our findings provide quantitative insights into the latency and throughput impacts of encryption protocols, and we propose four fundamental principles for security by design within Open RAN systems.
Finally, we address the security of Time-Sensitive Networking (TSN) in O-RAN. The O-RAN framework encourages multi-vendor solutions but increases the exposure of the open fronthaul (FH) to security risks, especially when deployed over third-party networks. Synchronization is crucial for reliable 5G links, with attacks on synchronization mechanisms posing significant threats. We demonstrate the impact of spoofing and replay attacks on Precision Time Protocol (PTP) synchronization, causing catastrophic failures in a production-ready O-RAN and 5G-compliant private cellular network. To counter these threats, we design an ML-based monitoring solution detecting various malicious attacks with over 97.5% accuracy, and outline additional security measures for the O-RAN environment.

Malith Jayaweera PhD Dissertation Defense
2024-08-06 10:00 - 11:00
Name:
Malith Jayaweera
Title:
Energy-Aware Transformations for Affine Programs on GPUs
Date:
8/6/2024
Time:
10:00:00 AM
Committee Members:
Prof. David Kaeli (Co-advisor)
Prof. Yanzhi Wang (Co-advisor)
Dr. Norman Rubin
Prof. Martin Kong (Ohio State University)
Abstract:
Graphics Processing Units (GPUs) have been increasingly used to accelerate workloads ranging from high performance computing to machine learning. Development of high-level programming languages, improved compilers, and runtime drivers have helped to accelerate the widespread adoption of GPUs. Given the wider adoption and ever-increasing computing capabilities, the power consumption of GPUs is quickly becoming a critical factor. Furthermore, the GPU micro-architecture differs from vendor to vendor, and even between hardware generations of the same vendor. Also, program variants with similar performance could differ in energy consumption due to the difference in utilization of GPU resources such as Streaming Multiprocessors (SMs) or memory. Despite performance improvements in compilation techniques, energy-aware code generation for heterogeneous GPUs has not been aggressively explored.
In this dissertation, we first identify the potential for energy-aware compilation techniques for GPUs. Next, we use these insights to study loop tiling, which is a popular loop transformation that has been successfully applied to computational domains such as linear algebra, deep neural networks and iterative stencils. We then propose an energy-aware tile size selection for affine programs to generate energy-efficient code targeting GPUs.
We also investigate the challenging problem of optimizing the scheduling of complex sparse tensor algebra and expressions on GPUs, with a focus on maximizing parallelism utilization to unlock optimal performance. We perform a comprehensive examination of the search space for sparse tensor expression scheduling, seeking to characterize the intricate inter-relationships between kernel characteristics, GPU architecture, and hardware constraints such as memory bandwidth limitations, to inform optimal scheduling decisions.

Kubra Alemdar PhD Dissertation Defense
2024-08-07 11:00 - 12:00
Name:
Kubra Alemdar
Title:
Overcoming and Engineering Wireless Signals for Communication and Computation
Date:
8/7/2024
Time:
11:00:00 AM
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Josep Jornet
Prof. Marvin Onabajo
Abstract:
The phenomenal growth of connected devices, especially rapid expansion of IoT networks and the increasing demand for wireless services are the main driving forces for the evolution of wireless technologies. However, the realization of such technologies requires a radical transformation of existing infrastructures to satisfy the needs of changing wireless environments. The main limitation in delivering these systems stems from a vast diversity in their demands and constraints. To address this limitation, this dissertation shows how wireless signals and their interaction with and within the wireless propagation domain can be used as communication or computational tools that enable us to achieve certain novel tasks. Specifically, we build i) cross-functionality architectures to engineer the wireless channel to a) enable the operation of emerging technologies, and b) demonstrate a new paradigm for computing with wireless signals, and ii) intelligently shape the wireless channel to create reliable communication links. This dissertation presents an experimentally validated software-hardware systems with thorough analysis, delivering the following key advancements with distinct contributions:
First, We present an innovative physical layer solution for distributed networks that provides over-the-air (OTA) clock synchronization, known as RFCLOCK, to overcome the hurdle of implementing fine-grained synchronization for emerging technologies. We first develop the theory for such precision synchronization, and second implement it in a custom-design, compatible with commercial-off-the-shelf (COTS) software-defined radios (SDRs). We compare the performance of RFClock with popular wired and GPS-based hardware solutions, both in terms of clock performance as well as impact on distributed beamforming.
Next, we propose two novel approaches, utilizing reconfigurable intelligent surfaces (RISs) to ensure reliable connectivity in wireless networks by controlling the propagation environment: i) we present RIS-based spatio-temporal approach to enhance the link reliability for IoTs where sensors are small-factor designs with single-antenna in a rich multipath environment. We demonstrate the design of RIS and how it can effectively perturb the environment, generating multiple wireless propagation channels and achieving the performance of a multi-antenna receiver in a Single-Input Single-Output (SISO) link. We compare the performance of the system with a multi-antenna receiver in terms of channel hardening and outage probability. ii) We introduce REMARKABLE, an online learning based adaptive beam selection strategy for robot connectivity that trains kernelized multi-armed bandit (MAB) model directly in real-world settings of a factory floor. We show how RISs with passive reflective elements can create beamforming towards target robots, and provide a solution to the problem of adaptive beam selection in dynamic channel conditions. We experimentally demonstrate that REMARKABLE can achieve a significant reduction in beam selection time compared to classical approaches and adaptive beam selection in mobility settings.
Finally, we introduce AirFC, a system harnessing the capability of OTA computation to run inference on a neural network (NN) consisting of a set of fully connected layers (FC) by leveraging multi-antenna systems. We experimentally demonstrate and validate that such computation is accurate enough when compared to its digital counterpart.

Cobra Alemdar PhD Dissertation Defense
2024-08-07 11:00 - 12:00

Northeast Regional Higher Education Neurodiversity Coalition Conference
2024-08-08 - 2024-08-09 | Interdisciplinary Science and Engineering Complex (ISEC), 805 Columbus Ave, Boston, MA, 02115, United States
Improving educational and professional outcomes for neurodivergent students
This conference is both timely and relevant. The neurodiverse population is increasing, while students have lower academic performance and higher college dropout and underemployment rates.
Join Together to Make a Difference

Increase your understanding of neurodiversity
Learn trends in neurodiversity employment
Understand best practices for neuroinclusive pedagogy and curriculum
Gain knowledge to create a neuroinclusive workplace and campus
Explore and share best practices with a community of higher education and industry professionals

Who Should Attend
First-year engineering faculty, deans, department chairs, teaching faculty, research assistants, academic and career advisors, DEI professionals, employers, and other college and workforce professionals.
Featured Keynote Speakers
Dr. Jeff Karp, a renowned biomedical engineer at Harvard Medical School and MIT grew up being “written off” for his learning differences. He consequently developed “Life Ignition Tools,” a process for embracing life that resulted from years of iteration and tinkering to make his unique thought patterns and behavior work for him.
John Elder Robison, a well-known author of Switched On: A Memoir of Brain Change and Emotional Intelligence, Look Me in the Eye, Be Different, and Raising Cubby, which details his life with Asperger syndrome. He’s a leading voice for autism and neurodiversity, imploring audiences to find strengths where others see weaknesses based on societal standards.
Learn More and Register
https://coe.northeastern.edu/nehenc.
Presented in partnership by Northeastern University, University of Connecticut, and the University of Rhode Island

Peiyan Dong PhD Dissertation Defense
2024-08-08 15:00 - 16:00
Name:
Peiyan Dong
Title:
Software-Hardware Co-Design: Towards Ultimate Efficiency in Deep Learning Acceleration
Date:
8/8/2024
Time:
3:00:00 PM
Committee Members:
Prof. Yanzhi Wang (Advisor)
Prof. David R. Kaeli
Prof. Devesh Tiwari
Prof. Cheng Tan
Abstract:
As AI techniques continue to advance, the efficient deployment of deep neural networks on resource-constrained devices becomes increasingly appealing yet challenging. Simultaneously, the proliferation of powerful AI technologies has raised significant concerns about sustainability and fairness, demanding increased attention from the community. This talk presents two novel software-hardware co-designs for improving the efficiency and sustainability of deep learning models. The first part introduces a hardware-efficient adaptive token pruning framework for Vision Transformers (ViTs) on embedded FPGA, HeatViT, which achieves significant speedup under similar model accuracy compared to the state-of-the-art. HeatViT is the first end-to-end accelerator for ViT on embedded FPGA and also achieve practical speedup by data-level compression for the first time. The second presents PackQViT and Agile-Quant, a paradigm of the efficient implementation for transformer-based models by sub-8-bit packed quantization and SIMD-based optimization for computing kernels. Our framework can achieve better task performance than state-of-the-art ViTs and LLMs with significant acceleration on edge processors, such as mobile CPU, Raspberry Pi and RISC-V. This work not only marks the first successful implementation of the LLM on the edge but also addresses the previous limitation where edge processors struggled to efficiently handle sub-8-bit computations. At the conclusion of the presentation, the speaker will discuss today’s challenges related to AI sustainability and fairness and outline her research plans aimed at addressing these issues.

Galante Program Virtual Info Sessions
2024-08-08 16:30 - 17:00
Learn how the Galante Engineering Business Program and Engineering Business Certificate can complement your technical engineering education with essential business skills.
Join us for an informational session to learn more about the Galante Engineering Business Program on one of the following dates:

Thursday, August 8 at 4:30 p.m. EDT – Virtual
Monday, August 12 at 8:30 a.m. EDT – Virtual
Tuesday, August 20 at 12:00 p.m. EDT – Virtual
Thursday, August 22 at 9:00 a.m. EDT – Virtual

RSVP Here
During the session, we will cover the details of the Galante Program and Engineering Business Certificate, including the application process and eligibility requirements.

Yifan Gong PhD Dissertation Defense
2024-08-09 11:00 - 12:00
Name:
Yifan Gong
Title:
Towards Energy-Efficient Deep Learning for Sustainable AI
Date:
8/9/2024
Time:
11:00:00 AM
Committee Members:
Prof. Yanzhi Wang (Advisor)
Prof. David R. Kaeli
Prof. Xue Lin
Prof. Huaizu Jiang
Prof. Stratis Ioannidis
Abstract:
The rapid advancements in deep learning (DL) and artificial intelligence (AI) have led to transformative applications across various domains, such as community virtual reality experiences, autonomous systems, and climate change prediction. Edge devices including mobile and embedded systems play a vital role in carrying these applications, facilitating the widespread adoption of machine intelligence. Along with the great success of DL and AI is the huge energy consumption for both training and inference. With the breakthrough of large-scale models for AI-generated content (AIGC) such as large language models and diffusion models, the energy consumption issue intensifies, causing the urgent need for sustainable AI solutions. In this talk, I will talk about how to facilitate deep learning on various edge devices in an energy-efficient manner for the goal of sustainable AI. Specifically, I will start by introducing my two system-level approaches to tackling the challenge. The first approach is named bottom-up, which conducts AI algorithm-aware efficient system design. The second approach is a top-down approach that achieves hardware-driven efficient AI algorithm design. Then, I will share my recent works addressing the efficiency issues for large-scale models. Finally, I will show the applications of my methods and pointers to the future direction.
