Huazhong University of Science & Technology, China
University at Buffalo, the State University of New York, USA
Energy Characterization and Optimization of
Embedded Data Mining Algorithms: A Case Study of
the DTW-kNN Framework
Presenter: Aosen Wang
Authors:
Hanqing Zhou, Lu Pu, Yu Hu, Xiaowei Xu, Huazhong University of S&T, China
Aosen Wang, Wenyao Xu, SUNY Buffalo, USA
Outline
1
Overview
1
Introduction of DTW-kNN
2
Energy Measurement Testbed
3
DTW-kNN Energy Characterization
4
DTW Energy Optimization
5
Conclusions
2
Overview (1/2)
 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.
3
Overview (2/2)
Our work: Energy characterization and optimization of DTW-kNN framework
 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.
4
Introduction of DTW-kNN (1/3)
DTW-kNN: a widely applied classification framework.
5
Introduction of DTW-kNN (2/3)
Dynamic Time Warping (DTW): a popular distance metric of similarity.
Two time series:
C = c1, c2, · · · , ci, · · · , cn, (1)
T = t1, t2, · · · , ti, · · · , tm.
DTW warping path:
M (i, j ) = (ci − tj )*(ci − tj )
6
Introduction of DTW-kNN (3/3)
k-Nearest Neighbors (kNN): well-investigated method for pattern classification.
7
Energy Measurement Testbed (1/2)
Framework:
ARM Cortex-M3:
STM32F103
Current-sense
Amplifier: MAX471
MCU:
MSP430
8
Energy Measurement Testbed (2/2)
Framework picture:
9
DTW-kNN Energy Characterization (1/2)
Characterization experiment setup:
 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
10
DTW-kNN Energy Characterization (2/2)
Energy characterization:
DTW calculation: as much as 97% !
Normalization
DTW
kNN
total energy
11
DTW Energy Optimization (1/7)
Experiment setup:
 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:
12
DTW Energy Optimization (2/7)
Optimization method: the squared distance
13
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.
14
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:
Hard to calculate
Easy to calculate
DTW1
>
LB1
DTW2
>
LB2
DTW3
>
LB3
 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
15
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 DTWkNN 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.
Huazhong University of Science & Technology, China
University at Buffalo, the State University of New York, USA
Thank you！
Aosen Wang
21