ECE 598

Fall 2019 All Classes

All Classes

Special Topics in ECE

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Credit: 0 TO 4 hours.

Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

May be repeated in the same or separate terms if topics vary.

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ECE 598 class schedule data for fall 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
72474	Lecture	HPN	12:30PM -1:50PM	TR	Electrical & Computer Eng Bldg	Mittal, R	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: High-Speed/Progrmable Networks Section Info: The ever-increasing demand for higher performance, new functionality, and flexibility has given rise to radical new designs for networking infrastructure, that not only unleash exciting new opportunities, but also challenge conventional wisdom. The goal of this course is to introduce students to such recent research and industrial advancements in networking. In each lecture, we will discuss one or two recent papers that propose (or use) unconventional new designs for network stack, network interface cards, or switches. The papers are systems oriented, focusing on practical challenges associated with designing and implementing such network systems, and cover latest topics such as programmable switches, kernel-bypass networking, RDMA, and smart NICs. Prerequisites : Understanding of basic concepts in computer networking. One of the following courses: ECE/CS 438 (Communication Networks) or ECE 428 / CS 425 (Distributed Systems).
72498	Lecture	MS	2:00PM -3:20PM	TR	Electrical & Computer Eng Bldg	Huang, J	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Adv Memory & Storage Systems Section Info: In this course, we will discuss advanced techniques for building memory and storage systems. The course will cover a variety of latest research topics centered around the memory and storage systems that include the new and emerging hardware architecture, memory/storage systems software, memory-centric applications, near-storage computing, rack-scale storage, storage security and reliability, mobile/wearable/IoT storage, and storage in large-scale data centers. Through this course, students will learn not only the fundamental concepts of memory and storage systems via the lecture materials, but also the hands-on experience of building and evaluating a memory/storage-centric system via projects. Prerequisites: One of the following: ECE 391, ECE 411, or ECE 511
72381	Lecture	NSG	11:00AM -12:20PM	TR	Electrical & Computer Eng Bldg	Shanbhag, N	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Deep Learning in Hardware Section Info: This course will present challenges in implementing deep learning algorithms on resource-constrained hardware platforms at the Edge such as wearables, IoTs, autonomous vehicles, and biomedical devices. Fixed-point requirements of deep for deep neural networks and convolutional neural networks including the back-prop based training will be studied. Algorithm-to-architecture mapping techniques will be explored to trade-off energy-latency-accuracy in deep learning digital accelerators and analog in-memory architectures. Fundamentals of learning behavior, fixed-point analysis, architectural energy and delay models will be introduced in just-in-time manner throughout the course. Case studies of hardware (architecture and circuit) realizations of deep learning systems will be presented. Homeworks will include a mix of analysis and programming exercises in Python and Verilog leading up to a term project. Prerequisites: ECE 313 and 482
72446	Lecture	SG	2:00PM -3:20PM	TR	Electrical & Computer Eng Bldg	Gupta, S	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Learning-Based Robotics Section Info: This course will introduce students to recent developments in the area of learning-based robotics. The course will start with an overview of background material from relevant subfields: computer vision, machine learning, robotics and control theory. Next, we will discuss advanced techniques for learning policies for robots, such as model-free reinforcement learning with function approximators, model learning, model-based RL with learned models, imitation learning, inverse reinforcement learning, self-supervised learning, exploration, and hierarchical reinforcement learning. These advanced techniques will be covered via recent research papers that develop and validate them. The course will conclude with case-studies on robotic navigation, and manipulation from recent papers. Project work as part of the course will provide a flavor of research in this new emerging area. Prerequisites: Understanding of basic concepts in artificial intelligence, and machine learning. Students must have taken at least one of the following courses: ECE 448 / CS 440 (Introduction to Artificial Intelligence), ECE 544NA (Pattern Recognition), ECE 549 / CS 543 (Computer Vision).
70563	Lecture-Discussion	WZ	9:30AM -10:50AM	TR	Electrical & Computer Eng Bldg	Zhu, W	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: 2D Material Electr & Photonics Section Info: 2D Material Electronics and Photonics. Two-dimensional (2D) material characterizations, 2D electronic devices, 2D optical devices. Prerequisite: ECE 340 or equivalent.
72081	Lecture	ZZ	2:00PM -3:20PM	TR	Electrical & Computer Eng Bldg	Zhao, Z	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: High Dimensional Geometric Dat Section Info: High Dimensional Geometric Data Analysis. This course aims to establish the mathematical foundation of many recent algorithms for tasks such as organization and visualization of data clouds, dimensionality reduction, clustering, and regression. Data analysis is an interdisciplinary field. It combines mathematics (both pure and applied), computer science (machine learning, theoretical CS, AI, computer vision), electrical engineering (signal and image processing), statistics, structural biology, neuroscience, computational biology (microarray data for gene expression), biophysics and chemical engineering (molecular dynamics simulations), and more. We will focus on a few particular methods and explain what they are good for, what are their limitations, what is the underlying math, in order to develop a good sense of when to apply them and develop a sound basis for designing new data analysis algorithms. The course will have three main sections: 1) high dimensional probability, 2) geometric data analysis, and 3) other recent advances with applications. The high-dimensional probability section of the course aims at getting insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. In the second part of the course, we introduce spectral methods that are useful in the analysis of big data sets. Particular applications involve cryo-electron microscopy single particle reconstruction and density functional theory with strongly correlated electrons. Prerequisite: ECE 534 or equivalents. Programming in Python (Matlab).