Global Arc

Analysis and design of digital integrated circuits using deep sub-micron CMOS technologies as well as emerging and post-CMOS technologies (Si finFETs, III-V, carbon). Emphasis on design, including synthesis, simulation, layout and post-layout verification. Analysis of energy, power, performance, area of logic-gates, interconnect and signaling structures.

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Theory of digital computing systems. Topics include logic function decomposition, reliability and fault diagnosis, synthesis of synchronous circuits and iterative networks, state minimization, synthesis of asynchronous circuits, state-identification and fault detection, finite-state recognizers, definite machines, information lossless machines. Three hours of lectures. Prerequisite: 206.

Component-level issues related to testing and design/synthesis for testability of digital systems. Topics include test generation for combinational and sequential circuits, design and synthesis for testability, and built-in self-test circuits. Three hours of lectures. Prerequisite 206.

Blockchains are decentralized digital trust engines that are the underlying technology behind Web3, a loosely defined denotation of the Internet architecture in the years to come, including decentralization of the platform economy of the modern Internet (Web2). In this course, we conduct a full-stack study of blockchains, viewing them as a whole integrated computer system involving networking, incentives, consensus, data structures, cryptography and memory management. The course uses the Bitcoin architecture as a basis to construct the foundational design and algorithmic principles of blockchains.

Smartphones are the de-facto computing and communications devices of tomorrow. They can access any information in cyberspace and perform any computations through cloud computing and locally. We study smartphone design and security through an architectural perspective. Topics include smartphone system architecture; System-on-Chip design; heterogeneous and multicore processors; sensors, multimedia, communications and storage subsystems; basic security concepts; hardware and software security in smartphones; security vulnerabilities; use and abuse of built-in sensors; associated wearables and Internet-of-Things; and security improvements.

Blockchains are digital platforms whose consistency and liveness are maintained by a decentralized set of participants. The combination of programmability, permissionless access and the financial nature of the underlying token (e.g., ETH in the Ethereum blockchain) has led to tremendous innovation in financial products on the blockchain, broadly covered under the rubric of decentralized finance or simply DeFi. The purpose of this course is to introduce these developments classified as "elements" of DeFi, from a computer science point of view. Periodic programming assignments provide a hands-on instruction to the technical material.

An in-depth study of the fundamentals of modern computer processor and system architecture. Students will develop a strong theoretical and practical understanding of modern, cutting-edge computer architectures and implementations. Studied topics include: Instruction-set architecture and high-performance processor organization including pipelining, out-of-order execution, as well as data and instruction parallelism. Cache, memory, and storage architectures. Multiprocessors and multicore processors. Coherent caches. Interconnection and network infrastructures. Prerequisite: ECE 375/COS 375 and ECE 206/COS 306 (or familiarity with Verilog).

Our society is increasingly transitioning towards an information-centric paradigm, enabled by pervasive networked computing devices. This has brought concerns about security and privacy to a forefront; attackers can undermine security and privacy by exploiting vulnerabilities in our systems and protocols. This course focuses on fundamental mechanisms that enable security. These include cryptographic mechanisms, architectural techniques, and network-level primitives. We will also study how to leverage interdisciplinary techniques from formal methods and machine learning to secure our systems.

With foundation built upon statistical and algebraic learning theory, this course offers an in-depth learning experience on machine learning for (big) data analysis for senior and graduate students in electrical engineering, computer science, and applied statistics - with some exposure to algebra and statistics. It covers various kernel-based unsupervised and supervised learning models and provides an integrated understanding of the mathematical theory and their potential applications. With the accompanied software learning laboratories. It also demonstrates how kernel learning models work for pattern recognition and data analysis.