Post on 17-Dec-2015
transcript
Energy Characterization and Optimization ofEmbedded Data Mining Algorithms: A Case Study ofthe DTW-kNN Framework
Huazhong University of Science & Technology, ChinaUniversity at Buffalo, the State University of New York, USA
Authors:Hanqing Zhou, Lu Pu, Yu Hu, Xiaowei Xu, Huazhong University of S&T, ChinaAosen Wang, Wenyao Xu, SUNY Buffalo, USA
Presenter: Aosen Wang
Introduction of DTW-kNN1
Energy Measurement Testbed2
3
DTW Energy Optimization4
Conclusions5
Outline
DTW-kNN Energy Characterization
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Overview1
The DTW-kNN framework is widely applied for classification in data
mining, such as speech recognition and financial market prediction.
DTW-kNN has not been studied on mobile platform or embedded system.
Overview (1/2)
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We design an energy measurement testbed for DTW-kNN algorithms.
We analyze the energy characterization of each component in the DTW-
kNN framework based on our proposed energy measurement testbed;
Three optimization strategies are proposed and implemented on the
testbed from algorithmic level to improve energy efficiency.
Overview (2/2)
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Our work: Energy characterization and optimization of DTW-kNN framework
Introduction of DTW-kNN (1/3)
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DTW-kNN: a widely applied classification framework.
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Dynamic Time Warping (DTW): a popular distance metric of similarity.
Introduction of DTW-kNN (2/3)
Two time series:C = c1, c2, · · · , ci, · · · , cn, (1)T = t1, t2, · · · , ti, · · · , tm.
DTW warping path: M (i, j ) = (ci − tj )*(ci − tj )
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k-Nearest Neighbors (kNN): well-investigated method for pattern classification.
Introduction of DTW-kNN (3/3)
Energy Measurement Testbed (1/2)
Framework:
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Current-sense Amplifier: MAX471
MCU: MSP430
ARM Cortex-M3:STM32F103
Energy Measurement Testbed (2/2)
Framework picture:
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DTW-kNN Energy Characterization (1/2)
Characterization experiment setup:
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5 datasets: from from a popular data warehouse;
Short sequence length: limited RAM and ROM
Memory-efficient operation method:
Memory Space requirement from 2×N×N to 2×N
DTW-kNN Energy Characterization (2/2)
Energy characterization:
DTW calculation: as much as 97% !
total energy
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Normalization
DTW
kNN
DTW Energy Optimization (1/7)
Experiment setup:
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All the selected and proposed methods have no influence on accuracy.
5 datasets, short sequence length, memory-efficient operation method;
k=1: k does not have significant influence on the energy characterization;
Training set: 10 and test set: 100;
Energy calculation:
DTW Energy Optimization (2/7)
Optimization method: the squared distance
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DTW Energy Optimization (3/7)
Optimization method: early abandon of DTW
There exist at least 1 element in a row that belongs to the warping path.
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DTW Energy Optimization (4/7)
Optimization method: lower bound and indexing DTW
Lower bounding functions are used to estimate the lower bound of DTW distances.
An example of how lower bound (LB) and indexing work:
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DTW1
DTW2
DTW3
LB1
LB2
LB3
>
>
>
Hard to calculate Easy to calculate
Calculate the 3 LBs and sort them;
Calculate the DTW with the lowest LB
LB2 is the lowest, so calculate DTW2
Compare DTW2 with LB1 and LB3
As DTW2 is smaller than LB1 and LB3,
DTW2<LB1<DTW1 and DTW2<LB3<DTW3
So calculations of DTW1 and DTW3 can be elimited
DTW Energy Optimization (5/7)
Optimization method: lower bound and indexing DTW
3 LB methods are adopted.
DTW Energy Optimization (6/7)
Optimization method: Put the methods all together
SD: Squared Distance; EA: Early Abandon; LB_***: Lower Bound method
DTW Energy Optimization (7/7)
Frequency scaling on dynamic energy:
Conclusion
In this paper, we investigate the energy characterization and optimization of
DTW-kNN framework from algorithmic level.
The bottleneck of the DTW-kNN framework is distance matrix calculation
accounting for 89.14% on average of the total energy consumption.
The energy reduction of squared distance, early abandon and lower bounding
methods are about 1%, from 29.5% to 89.9% and about 50% respectively.
When all optimization methods are implemented, the energy reduction can be
as much as 74.6%.
Future Work
We will continue our work by another two aspects to improve energy efficiency:
Architecture-level: parallel computing of each template.
Microarchitecture-level: hardware accelerator, such as speeding up the distance matrix
calculation and warping path calculation.
Thank you!
Huazhong University of Science & Technology, ChinaUniversity at Buffalo, the State University of New York, USA
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Aosen Wang