1. Energy Characterization and Optimization of Embedded Data Mining Algorithms: A Case Study of the DTW-kNN Framework Huazhong University of Science & Technology, China University at Buffalo, the State University of New York, USA Authors: Hanqing Zhou, Lu Pu, Yu Hu, Xiaowei Xu, Huazhong University of S&T, China Aosen Wang, Wenyao Xu, SUNY Buffalo, USA Presenter: Aosen Wang

2. Introduction of DTW-kNN 1 Energy Measurement Testbed 2 3 DTW Energy Optimization 4 Conclusions 5 Outline DTW-kNN Energy Characterization 2 Overview 1

3.  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) 3

4.  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) 4 Our work: Energy characterization and optimization of DTW-kNN framework

5. Introduction of DTW-kNN (1/3) 5 DTW-kNN: a widely applied classification framework.

6. 6 Dynamic Time Warping (DTW): a popular distance metric of similarity. Introduction of DTW-kNN (2/3) Two time series: C = c 1, c 2, · · ·, c i, · · ·, c n, (1) T = t 1, t 2, · · ·, t i, · · ·, t m. DTW warping path: M (i, j ) = (ci − tj )*(ci − tj )

7. 7 k-Nearest Neighbors ( kNN): well-investigated method for pattern classification. Introduction of DTW-kNN (3/3)

8. Energy Measurement Testbed (1/2) Framework: 8 Current-sense Amplifier: MAX471 MCU: MSP430 ARM Cortex-M3: STM32F103

9. Energy Measurement Testbed (2/2) Framework picture: 9

10. DTW-kNN Energy Characterization (1/2) Characterization experiment setup: 10  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

11. DTW-kNN Energy Characterization (2/2) Energy characterization: DTW calculation: as much as 97% ! total energy 11 Normalization DTW kNN

12. DTW Energy Optimization (1/7) Experiment setup: 12  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:

13. DTW Energy Optimization (2/7) Optimization method: the squared distance 13

14. 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

15. 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: 15 DTW1 DTW2 DTW3 LB1 LB2 LB3 > > > Hard to calculateEasy 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

16. DTW Energy Optimization (5/7) Optimization method: lower bound and indexing DTW 3 LB methods are adopted.

17. DTW Energy Optimization (6/7) Optimization method: Put the methods all together SD: Squared Distance; EA: Early Abandon; LB_***: Lower Bound method

18. DTW Energy Optimization (7/7) Frequency scaling on dynamic energy:

19. 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%.

20. 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.

21. Thank you ！ Huazhong University of Science & Technology, China University at Buffalo, the State University of New York, USA 21 Aosen Wang