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Neuromorphic System Design and Application

Liu, Beiye (2016) Neuromorphic System Design and Application. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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With the booming of large scale data related applications, cognitive systems that leverage modern data processing technologies, e.g., machine learning and data mining, are widely used in various industry fields. These application bring challenges to conventional computer systems on both semiconductor manufacturing and computing architecture. The invention of neuromorphic computing system (NCS) is inspired by the working mechanism of human-brain. It is a promising architecture to combat the well-known memory bottleneck in Von Neumann architecture. The recent breakthrough on memristor devices and crossbar structure made an important step toward realizing a low-power, small-footprint NCS on-a-chip. However, the currently low manufacturing reliability of nano-devices and circuit level constrains, .e.g., the voltage IR-drop along metal wires and analog signal noise from the peripheral circuits, bring challenges on scalability, precision and robustness of memristor crossbar based NCS.
In this dissertation, we quantitatively analyzed the robustness of memristor crossbar based NCS when considering the device process variations, signal fluctuation and IR-drop. Based on our analysis, we will explore deep understanding on hardware training methods, e.g., on-device training and off-device training. Then, new technologies, e.g., noise-eliminating training, variation-aware training and adaptive mapping, specifically designed to improve the training quality on memristor crossbar hardware will be proposed in this dissertation. A digital initialization step for hardware training is also introduced to reduce training time. The circuit level constrains
will also limit the scalability of a single memristor crossbar, which will decrease the efficiency of implementation of NCS. We also leverage system reduction/compression techniques to reduce the required crossbar size for certain applications. Besides, running machine learning algorithms on embedded systems bring new security concerns to the service providers and the users. In this dissertation, we will first explore the security concerns by using examples from real applications. These examples will demonstrate how attackers can access confidential user data, replicate a sensitive data processing model without any access to model details and how expose some key features of training data by using the service as a normal user. Based on our understanding of these security concerns, we will use unique property of memristor device to build a secured NCS.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Liu, Beiyebel34@pitt.eduBEL34
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChen,
Committee MemberLi,
Committee MemberSejdić, Ervinesejdic@pitt.eduESEJDIC
Committee MemberMao, Zhi-Hongzhm4@pitt.eduZHM4
Committee MemberLi,
Date: 15 June 2016
Date Type: Publication
Defense Date: 30 March 2016
Approval Date: 15 June 2016
Submission Date: 28 March 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 116
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: memristor, neuromorphic
Date Deposited: 15 Jun 2016 18:23
Last Modified: 15 Nov 2016 14:32


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