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Power reduction by varying sampling rate
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Source International Symposium on Low Power Electronics and Design archive
Proceedings of the 2005 international symposium on Low power electronics and design table of contents
San Diego, CA, USA
SESSION: Low power software design and sensing table of contents
Pages: 227 - 232  
Year of Publication: 2005
ISBN:1-59593-137-6
Authors
William R. Dieter  University of Kentucky, Lexington, KY
Srabosti Datta  University of Kentucky, Lexington, KY
Wong Key Kai  University of Kentucky, Lexington, KY
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

The rate at which a digital signal processing (DSP) system operates depends on the highest frequency component in the input signal. DSP applications must sample their inputs at a frequency at least twice the highest frequency in the input signal (i.e., the Nyquist rate) to accurately reproduce the signal. Typically a fixed sampling rate, guaranteed to always be high enough, is used. However, an input signal may have periods when the signal has little high frequency content as well as periods of silence. When the input signal has no perceptible high frequency components, the system can reduce its sampling rate, thereby reducing the number of samples processed per second, allowing the CPU speed to be scaled down without reducing output quality. This paper describes how to reduce power consumption in DSP applications by varying the amount of processing based on the input signal, and reports results of experiments with a prototype implementation. Experiments with a prototype show that when the system performs little processing, the added overhead of the variable sampling rate technique increased power consumption. When the system performs more processing, 18 FIR filters per frame, the power consumption was reduced to 40% of the power required for a static sampling rate, while not reducing sound quality


REFERENCES

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Collaborative Colleagues:
William R. Dieter: colleagues
Srabosti Datta: colleagues
Wong Key Kai: colleagues