Neuromorphic Analog VLSI Circuits


CMPE Degree: This course is Not Applicable for the CMPE degree.

EE Degree: This course is Not Applicable for the EE degree.

Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.

Technical Interest Group(s) / Course Type(s): Electronic Design and Applications

Course Coordinator:

Prerequisites: ECE 3050

Corequisites: None.

Catalog Description

Large-scale analog computation for sensory and motor processing. Analog
building blocks are presented, leading to VLSI systems inspired by
neurobiological architectures and computational paradigms.

Course Outcomes

Not Applicable

Student Outcomes

In the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.

1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics

2. ( Not Applicable ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors

3. ( Not Applicable ) An ability to communicate effectively with a range of audiences

4. ( Not Applicable ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts

5. ( Not Applicable ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives

6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Strategic Performance Indicators (SPIs)

Outcome 1 (Students will demonstrate expertise in a subfield of study chosen from the fields of electrical engineering or computer engineering):
1. Students will demonstrate the tradeoffs between various neuromorphic implementations starting at a fundamental circuit level through larger system design.

Outcome 2 (Students will demonstrate the ability to identify and formulate advanced problems and apply knowledge of mathematics and science to solve those problems):
1. Students will demonstrate analytical modeling of neuromorphic blocks, including the various aspects of computational neuroscience and analog electrical modeling and the relationships between these blocks

Outcome 3 (Students will demonstrate the ability to utilize current knowledge, technology, or techniques within their chosen subfield):
1. Students will demonstrate utilizing computer design tools and experimental measurement opportunities in the design of neuromorphic architectures, as well as in the design of full analog systems.

Course Objectives

Topical Outline

MOS Transistor Operation
Weak Inversion
Strong Inversion

Floating-Gate Circuits
Electron Tunneling
Hot-Electron Injection
Fundamental Adaptive Circuits

Transcondutance Amplifiers
Building Blocks: Current Mirror, Differential Pair, Gain Stages
DC Operation

Time-Domain Circuits
Second-Order Sections

Current-Mode Circuits
Translinear Principle
Floating-Gate MOS Circuits
Bump Circuit
Current Multipliers

Signal-Aggregation Circuit Arrays
Centroid Circuits
Resistive Networks
Diffusor Circuits
Winner-Take-All Circuits
Delay Lines

Neuromorphic Systems
Electronic Cochlea
Auditory Localization
Silicon Retinas: Voltage and Current Mode
Neuron Models
Address Event Communication
Motor Pattern Generation