Abstract

New approaches are sought to maximize the signal sensing and reconstruction performance of Internet-of-Things (IoT) devices while reducing their dynamic and leakage energy consumption. Recently, Compressive Sensing (CS) has been proposed as a technique aimed at reducing the number of samples taken per frame to decrease energy, storage, and data transmission overheads. CS can be used to sample spectrally-sparse wide-band signals close to the information rate rather than the Nyquist rate, which can alleviate the high cost of hardware performing sampling in low-duty IoT applications. In my dissertation, I am focusing mainly on the adaptive signal acquisition and conversion circuits utilizing spin-based devices to achieve a highly-favorable range of accuracy, bandwidth, miniaturization, and energy trade-offs while co-designing the CS algorithms. The use of such approaches specifically targets new classes of Analog to Digital Converter (ADC) designs providing Sampling Rate (SR) and Quantization Resolution (QR) adapted during the acquisition by a cross-layer strategy considering both signal and hardware-specific constraints. Extending CS and Non-uniform CS (NCS) methods using emerging devices is highly desirable. Among promising devices, the 2014 ITRS Magnetism Roadmap identifies nanomagnetic devices as capable post-CMOS candidates, of which Magnetic Tunnel Junctions (MTJs) are reaching broader commercialization. Thus, my doctoral research topic is well-motivated by the established aims of academia and industry. Furthermore, the benefits of alternatives to von-Neumann architectures are sought for emerging applications such as IoT and hardware-aware intelligent edge devices, as well as the application of spintronics for neuromorphic processing. Thus, in my doctoral research, I have also focused on realizing post-fabrication adaptation, which is ubiquitous in post-Moore approaches, as well as mission-critical, IoT, and neuromorphic applications.

Notes

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Graduation Date

2020

Semester

Spring

Advisor

DeMara, Ronald

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0008033; DP0023173

URL

https://purls.library.ucf.edu/go/DP0023173

Language

English

Release Date

May 2020

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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