Sun, Yuwen
(2010)
RNA: REUSABLE NEURON ARCHITECTURE FOR ON-CHIP ELECTROCARDIOGRAM CLASSIFICATION AND MACHINE LEARNING.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Artificial neural networks (ANN) offer tremendous promise in classifying electrocardiogram (ECG) for detection and diagnosis of cardiovascular diseases. In this thesis, we propose a reusable neuron architecture (RNA) to enable an efficient and cost-effective ANN-based ECG processing by multiplexing the same physical neurons for both feed-forward and back-propagation stages. RNA further conserves the area and resources of the chip and reduces power dissipation by coalescing different layers of the neural network into a single layer. Moreover, the microarchitecture of each RNA neuron has been optimized to maximize the degree of hardware reusability by fusing multiple two-input multipliers and a multi-input adder into one two-input multiplier and one two-input adder. With RNA, we demonstrated a hardware implementation of a three-layer 51-30-12 artificial neural network using only thirty physical RNA neurons.A quantitative design space exploration in area, power dissipation, and speed between the proposed RNA and three other implementations representative of different reusable hardware strategies is presented and discussed. An RNA ASIC was implemented using 45nm CMOS technology and verified on a Xilinx Virtex-5 FPGA board. Compared with an equivalent software implementation in C executed on a mainstream embedded microprocessor, the RNA ASIC improves both the training speed and the energy efficiency by three orders of magnitude, respectively. The real-time and functional correctness of RNA was verified using real ECG signals from the MIT-BIH arrhythmia database.
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| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
25 June 2010 |
| Date Type: |
Completion |
| Defense Date: |
5 April 2010 |
| Approval Date: |
25 June 2010 |
| Submission Date: |
21 March 2010 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical Engineering |
| Degree: |
MSEE - Master of Science in Electrical Engineering |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
ANN; ASIC; ECG; FPGA; machine learning |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-03212010-201342/, etd-03212010-201342 |
| Date Deposited: |
10 Nov 2011 19:32 |
| Last Modified: |
15 Nov 2016 13:37 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/6543 |
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