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Brain Inspired Artificial Intelligence

CPE 4953

The course will offer insights into how brain physiology and information processing give rise to higher-order intelligence. We will study single neuronal models, spiking neural networks, deep learning, supervised/unsupervised learning rules, temporal signal classification and prediction and large-scale neuromorphic accelerators.

Spring 2021, 2022

AI Practicum

EE 6973

The practicum offers experiential learning in the context of AI for real world. Industry partnerships might be leveraged, as deemed appropriate, to design an AI mini project. Standard AI programming tool suites, deployment considerations, and design flow will be learnt through the mini-project. Students will also be introduced to AI deployment challenges, responsible AI, and AI ethics.

Fall 2022

Brain Inspired Computing

CMPE 789

In this course students will acquire skills in modeling AI systems inspired by neural processes and apply this knowledge to real-world cognitive tasks. Unlike a computer, the human brain is massively parallel, continually processes and learns from noisy, streaming data, operates on an ultra-low energy budget and exhibits a high degree of robustness. The rapidly growing field of brain-inspired AI seeks to achieve these same qualities in artificial systems.

2014 - 2019

Digital IC Design

CMPE 796

This course will cover the basic theory and techniques of Digital Integrated Circuit Design in CMOS technology. Topics include CMOS transistor theory and operation, design and implementation of CMOS circuits, fabrication process, layout and physical design, delay and power models, static and dynamic logic families, testing and verification, memory and nanoscale technologies. Laboratory assignments and project facilitate in hands-on learning of circuit-level design and simulation, layout and parasitic extractions, pre and post-layout verification and validation.

2012 - 2016

VLSI Design

CMPE 0306-530/630

An introduction to the design and implementation of Very Large Scale Integration (or VLSI), including NMOS and PMOS devices, CMOS circuits and digital subsystems. The procedures for designing and implementing digital integrated systems will be covered including the Mead and Conway structured design approach consisting of the use of stick diagramming, scaling of CMOS design rules and techniques for estimating time delays. Emphasis will be placed on the use of static CMOS circuits and regular structures such as programmed logic arrays in custom and standard cell-based designs.

2011 - 2012

Hardware Description Languages

CMPE 0306-351

This course is a more in depth coverage of current logic design and verification methodologies using a modern hardware description language (HDL). Topics include: coding for different levels of abstraction; implementation of arithmetic circuits and finite state machines; hierarchical designs; reusable component design; data and control path; best coding practices; design constraints and verification.

2012-2014

Low Power Design

CMPE 0306-732

This course covers the theory and practical aspects of low-power integrated circuit design in CMOS technology. Topics include: estimation and modeling power dissipation in CMOS circuits at different design abstractions, power optimization techniques with emphasis on transistor and system level, energy efficient SRAM cells and cache designs, low power design methodology, effect of device scaling, process variations and their impact on power optimization, and post-CMOS logic gates.

2012-2014

Introduction to Digital Systems

CMPE 0306-341

This course introduces the student to the basic components and methodologies used in digital systems design. It is usually the student's first exposure to engineering design. The laboratory component consists of small design, implement, and debug projects. The complexity of these projects increases steadily throughout the term, starting with circuits of a few gates, until small systems containing several tens of gates and memory elements.

2012-2014

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