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Cache-aware optimization of BAN applications
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International Conference on Hardware Software Codesign archive
Proceedings of the 6th IEEE/ACM/IFIP international conference on Hardware/Software codesign and system synthesis table of contents
Atlanta, GA, USA
SESSION: Case studies and industrial practices table of contents
Pages 149-154  
Year of Publication: 2008
ISBN:978-1-60558-470-6
Authors
Yun Liang  National University of Singapore, Singapore, Singapore
Lei Ju  National University of Singapore, Singapore, Singapore
Samarjit Chakraborty  National University of Singapore, Singapore, Singapore
Tulika Mitra  National University of Singapore, Singapore, Singapore
Abhik Roychoudhury  National University of Singapore, Singapore, Singapore
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
SIGBED: ACM Special Interest Group on Embedded Systems
ACM: Association for Computing Machinery
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
Publisher
ACM  New York, NY, USA
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ABSTRACT

Body-area sensor network or BAN-based health monitoring is increasingly becoming a popular alternative to traditional wired biomonitoring techniques. However, most biomonitoring applications need continuous processing of large volumes of data, as a result of which both power consumption and computation bandwidth turn out to be serious constraints for sensor network platforms. This has resulted in a lot of recent interest in design methods, modeling and software analysis techniques specifically targeted towards BANs and applications running on them. In this paper we show that appropriate optimization of the application running on the communication gateway of a wireless BAN and accurate modeling of the microarchitectural details of the gateway processor can lead to significantly better resource usage and power savings. In particular, we propose a method for deriving the optimal order in which the different sensors feeding the gateway processor should be sampled, to maximize cache re-use. Our case study using a faint fall detection application - from the geriatric care domain - which is fed by a number of smart sensors to detect physiological and physical gait signals of a patient show very attractive energy savings in the underlying processor. Alternatively, our method can be used to improve the sampling frequency of the sensors, leading to higher reliability and better response time of the application.


REFERENCES

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Collaborative Colleagues:
Yun Liang: colleagues
Lei Ju: colleagues
Samarjit Chakraborty: colleagues
Tulika Mitra: colleagues
Abhik Roychoudhury: colleagues