Meet Your Graduate Student Ambassadors!
2020-10-15 - 2020-12-30
Meet your Student Ambassadors! Prospective and Admitted Graduate Students are invited to meet their Student Ambassador via Unibuddy.

Cooperative Education Webinar
2020-12-16 08:00 - 09:00 (America/New_York)
You’re invited to learning more about the Graduate Cooperative Education program in the College of Engineering. The webinar will feature a talk by Assistant Dean of Co-op, Lorraine Mountain.
Cooperative education has been a historical strength of Northeastern University’s experiential learning brand. Each year students at every level and on every campus location participate in this signature model of experiential learning.
Our global presence spans all seven continents allowing our co-op employer partners and students alike to gain early access in tapping the increasingly competitive job market on a global stage. Northeastern’s vast network yields a multitude of global cooperative education opportunities across all industries and sectors.

WEBINAR DETAILS:
Topic: Co-op at Northeastern University
Date: Wednesday, December 16
Time: 8:00 – 9:00 AM EST

WEBINAR REGISTRATION INSTRUCTIONS:
https://us02web.zoom.us/webinar/register/WN_gfvqKoxIQfOHhLYnattN5g

ECE MS Thesis Defense: Jinghan Zhang
2020-12-16 09:30 - 10:30
MS Thesis Defense: Allocating One Common Accelerator-Rich Platform for Many Streaming Applications
Jinghan Zhang
Location: Zoom Link
Abstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain, e.g. video analytics, software-defined radio, and radar. This opens the opportunity for specialization (e.g. heterogeneous computing) to achieve the needed efficiency and/or performance. However, current Design Space Exploration (DSE) focuses on an individual application in isolation (e.g. one particular vision flow), but not a set of similar applications. Hence, optimizations that occur due to considering multiple applications simultaneously are missed. New DSE methodologies and tools are needed with a broader scope of application sets instead of individual applications.
This thesis introduces a novel Domain DSE approach focusing on streaming applications. Key contributions are: (1) a formalized method to extract the functional and structural similarities of domain applications, (2) domain application generation to provide enough synthetic domains as study cases, (3) a rapid platform performance estimation and comparison at two abstraction levels: Domain Score (DS) and Analytic Performance Estimation (APE) model, (4) a methodology to evaluate a platform’s benefit for a set of applications, and (5) two novel algorithms, Dynamic Score Selection (DSS) and GenetIc Domain Exploration (GIDE), for hardware/software partitioning of a domain-specific platform to maximize the throughput across domain applications (under certain constraints).
This thesis demonstrates DSS’s and GIDE’s benefits using OpenVX applications and synthetic domains. The DSS and GIDE generated domain-specific platforms improve performance over application-specific platforms by 58%, and 75% for OpenVX, as well as by 23% and 48% for synthetic applications. GIDE’s platforms reach 99.8% (OpenVX) and 97.6% (synthetic) throughput of the domain optimal platform obtained through exhaustive search.

ECE PhD Proposal Review: Majid Sabbagh
2020-12-16 10:00 - 11:00
PhD Proposal Review: The perils of shared computing: A hardware security perspective
Majid Sabbagh
Location: Teams Link
Abstract: The enormous computation power of modern processors and accelerators has rendered them shared computing resources for multiple users and applications, both in the cloud and on the edge. Despite software techniques for security such as virtualization and containers, recently a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources, posing serious threats to shared computing.
Fault attacks (FAs) and Side-Channel Attacks (SCAs) are two hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters, such as power consumption, electromagnetic emanations, and timing, to breach the confidentiality of the application.
In this dissertation, we introduce a new class of FAs against Graphics Processing Units (GPUs), called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is deep neural network (DNN) inference. In modern GPUs that support multiple kernels, the adversary is able to track the execution of the victim DNN through shared resources and control the timing of fault injections precisely. We launch a successful attack on a convolutional neural network kernel running on an NVIDIA RTX 2080 SUPER GPU with misclassifications. We further study the characteristics of fault injections and the fault propagation through the network.
We evaluate a timing side-channel attack called Prime+Probe attack on Central Processing Units (CPUs) and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that analyzes an x86 program’s memory accesses. It records and analyzes the memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns corresponding to the Prime+Probe attack.
Finally, I propose an FPGA-based RISC-V processor prototype as an evaluation platform for various cache timing attacks and transient attacks, and implement a taint tracking-based countermeasure against transient attacks. For the first phase, we have ported spectre v1 and v2 and return-stack-buffer attack to the SonicBOOM RISC-V processor.

ECE PhD Proposal Review: Hongjia Li
2020-12-16 10:00 - 11:00
PhD Proposal Review: Automation Design and DNN Acceleration Algorithms: From Software Implementation to Hardware Physical Design
Hongjia Li
Location: Zoom Link
Abstract: Deep learning has been growing at a fast pace in recent years and has been expanded into many application fields, with a wide range from image recognition, object detection to medical applications. Meanwhile, edging devices such as mobile devices are rapidly becoming the central computer and carrier for deep learning tasks. However, real-time execution has been limited due to the computation/storage resource constraints on these devices.
In this proposal review, I will dive into some aspects of DNN acceleration methods, including model compression techniques and software implementation optimizations. The goal is to achieve an unprecedented, real-time performance of large-scale neural network inference on edging devices. Additionally, an efficient physical design automation design is introduced for Adiabatic Quantum-Flux-Parametron (AQFP) circuits, meeting the unique features and constraints.

ECE PhD Proposal Review: Pu Zhao
2020-12-16 14:00 - 15:00
PhD Proposal Review: Towards Robust Image Classification with Deep learning and Real-Time DNN Inference on Mobile
Pu Zhao
Location: Zoom Link
Abstract: As the rapidly increasing popularity of deep learning, deep neural networks (DNN) have become the fundamental and essential building blocks in various applications such as image classification and object detection. However, there are two main issues which potentially limit the wide application of DNNs: 1) the robustness of DNN models raises security concerns, and 2) the large computation and storage requirements of DNN models lead to difficulties for its wide deployment on popular yet resource-constrained devices such as mobile phones.
To investigate the DNN robustness, we explore the DNN attack, robustness evaluation and defense. More specifically, for DNN attack, we achieve various attack goals (e.g. adversarial examples and fault sneaking attacks) with different algorithms (e.g. alternating direction method of multipliers (ADMM) and natural gradient descent (NGD) attacks) under various conditions (white-box and black-box attacks). For robustness evaluation, we propose a fast evaluation method to obtain the model perturbation bound such that any model perturbation within the bound does not alter the model classification outputs or incur model mis-behaviors. For the DNN defense, we investigate the defense performance with model connection techniques and successfully mitigate the fault sneaking and backdoor attacks.
With a deeper understanding of the DNN robustness, we further explore the deployment problem of DNN models on edge devices with limited resources. To satisfy the storage and computation limitation on edge devices, we adopt model pruning to remove the redundancy in models, thus reducing the storage and computation during inference. Besides, as some applications have real-time requirements with high inference speed sensitivities such as object detection on autonomous cars, we further try to implement real-time DNN inference for various DNN applications on mobile devices with pruning and compiler optimization. To summary, we mainly investigate the DNN robustness and implement real-time DNN inference on the mobile.

ECE PhD Proposal Review: Amirreza Farnoosh
2020-12-16 14:00 - 15:00
PhD Proposal Review: Unsupervised Learning of Low-Dimensional Dynamical Representations from Spatiotemporal Data
Amirreza Farnoosh
Location: Zoom Link
Abstract: Ever-improving sensing technologies offer a fast and accurate collection of large-scale spatiotemporal data, recorded from multimodal sensors of heterogeneous natures, in various application domains, ranging from medicine and biology to robotics and traffic control. In this proposal, we are learning the underlying representation of these data in an unsupervised manner, tailored towards several emerging applications, namely indoor navigation and mapping, neuroscience hypothesis testing, and time series segmentation and forecasting.
As such, (1) we present an unsupervised framework for real-time depth and view-angle estimation from an inertially augmented video recorded from an indoor scene by employing geometric-based machine learning and deep learning models. (2) We introduce a hierarchical deep generative factor analysis framework for temporal modeling of neuroimaging datasets. Our model approximates high dimensional data by a product between time-dependent weights and spatially dependent factors which are in turn represented in terms of lower dimensional latents. This framework can be extended to perform clustering in the low dimensional temporal latent or perform factor analysis in the presence of a control signal. (3) We present a deep switching dynamical system for dynamical modeling of multidimensional time-series data. Specifically, we employ a deep vector auto-regressive latent model switched by a chain of discrete latents to capture higher-order multimodal latent dependencies. This results in a flexible model that (i) provides a collection of potentially interpretable states abstracted from the process dynamics, and (ii) performs short- and long-term vector time series prediction in a complex multi-relational setting.
