A 128 channel 290 GMACs/W machine learning based co-processor for intention decoding in brain machine interfaces | IEEE Conference Publication | IEEE Xplore

A 128 channel 290 GMACs/W machine learning based co-processor for intention decoding in brain machine interfaces


Abstract:

A machine learning co-processor in 0.35μm CMOS for motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine alg...Show More

Abstract:

A machine learning co-processor in 0.35μm CMOS for motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm, time delayed sample based feature dimension enhancement, low-power analog processing and massive parallelism, it achieves an energy efficiency of 290 GMACs/W at a classification rate of 50 Hz. A portable external unit based on the proposed co-processor is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3%. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels.
Date of Conference: 24-27 May 2015
Date Added to IEEE Xplore: 30 July 2015
Electronic ISBN:978-1-4799-8391-9

ISSN Information:

Conference Location: Lisbon, Portugal

References

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