Song, Chang
(2017)
A quantization-aware regularized learning method in multi-level memristor-based neuromorphic computing system.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Neuromorphic computing, a VLSI realization of neuro-biological architecture, is inspired by the working mechanism of human-brain. As an example of a promising design methodology, synapse design can be greatly simplified by leveraging the similarity between the biological synaptic weight of a synapse and the programmable resistance (memristance) of a memristor. However, programming the memristors to the target values can be very challenging due to the impact of device variations and the limitation of the peripheral CMOS circuitry. A quantization process is used to map analog weights to discrete resistance states of the memristors, which introduces a quantization loss. In this thesis, we propose a regularized learning method that is able to take into account the deviation of the memristor-mapped synaptic weights from the target values determined during the training process. Experimental results obtained when utilizing the MNIST data set show that compared to the conventional learning method which considers the learning and mapping processes separately, our learning method can substantially improve the computation accuracy of the mapped two-layer multilayer perceptron (and LeNet-5) on multi-level memristor crossbars by 4.30% (11.05%) for binary representation, and by 0.40% (8.06%) for three-level representation.
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| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
13 June 2017 |
| Date Type: |
Publication |
| Defense Date: |
3 April 2017 |
| Approval Date: |
13 June 2017 |
| Submission Date: |
30 March 2017 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
36 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Memristor Crossbar; Regularization; Quantization; Neuromorphic Computing; Neural Networks; |
| Date Deposited: |
13 Jun 2017 15:58 |
| Last Modified: |
13 Jun 2017 15:58 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/31080 |
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