Cheng, Hsin-Pai
(2017)
Exploring the Optimal Learning Technique for IBM Neurosynaptic System to Overcome Quantization Loss.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Inspired by the fact that human brain is much more efficient than any nowadays computers, neuromorphic computing is aim at performing near human brain ability of processing huge amount of data in an extreme short time. For the hardware part, neuromorphic computing is also extended to systems facilitating the computation of neural network and machine learning algorithms. Recently, IBM Neurosynaptic system is one of the well-known project that dedicated on energy-efficient neural network applications. However, However, one of the known issues in TrueNorth design is the limited precision of synaptic weights, each of which can be selected from only four integers. To improve the computation accuracy and reduce the incurred hardware cost, in this work, we investigate seven different regularization functions in the cost function of the learning process on TrueNorth platform. Our experimental results proved that the proposed techniques considerably improve the computation accuracy of TrueNorth platform and reduce the incurred hardware and performance overheads.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
13 June 2017 |
| Date Type: |
Publication |
| Defense Date: |
29 November 2016 |
| Approval Date: |
13 June 2017 |
| Submission Date: |
7 April 2017 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
29 |
| 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: |
machine learning |
| Date Deposited: |
13 Jun 2017 15:19 |
| Last Modified: |
13 Jun 2018 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/31337 |
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