Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Exploring the Optimal Learning Technique for IBM Neurosynaptic System to Overcome Quantization Loss

Cheng, Hsin-Pai (2017) Exploring the Optimal Learning Technique for IBM Neurosynaptic System to Overcome Quantization Loss. Master's Thesis, University of Pittsburgh. (Unpublished)

Download (582kB) | Preview


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.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Cheng, Hsin-Paihsc38@pitt.eduhsc38
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChen, Yiranyic52@pitt.eduyic52
Committee MemberHai, Lihal66@pitt.eduhal66
Committee Memberzhi-hong, maozhm4@pitt.eduzhm4
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


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item