TY - GEN
T1 - A matrix-multiplying ADC implementing a machine-learning classifier directly with data conversion
AU - Zhang, Jintao
AU - Wang, Zhuo
AU - Verma, Naveen
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/3/17
Y1 - 2015/3/17
N2 - Embedded sensing systems conventionally perform A-to-D conversion followed by signal analysis. In many applications, the analysis of interest is inference (e.g., classification), but the sensor signals involved are too complex to model analytically. Machine learning is gaining prominence because it enables data-driven training of classifiers, overcoming the need for analytical models. This work presents: 1) an algorithmic formulation, where feature extraction and classification are combined into a single matrix, reducing the total multiplications needed, and 2) a matrix-multiplying ADC (MMADC) that enables multiplication of input samples by a programmable matrix. Thus, the MMADC combines feature extraction and classification with data conversion, mitigating the need for further computations. Two systems are demonstrated: an ECG-based cardiac-arrhythmia detector and an image-pixel-based gender detector.
AB - Embedded sensing systems conventionally perform A-to-D conversion followed by signal analysis. In many applications, the analysis of interest is inference (e.g., classification), but the sensor signals involved are too complex to model analytically. Machine learning is gaining prominence because it enables data-driven training of classifiers, overcoming the need for analytical models. This work presents: 1) an algorithmic formulation, where feature extraction and classification are combined into a single matrix, reducing the total multiplications needed, and 2) a matrix-multiplying ADC (MMADC) that enables multiplication of input samples by a programmable matrix. Thus, the MMADC combines feature extraction and classification with data conversion, mitigating the need for further computations. Two systems are demonstrated: an ECG-based cardiac-arrhythmia detector and an image-pixel-based gender detector.
UR - http://www.scopus.com/inward/record.url?scp=84940733990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940733990&partnerID=8YFLogxK
U2 - 10.1109/ISSCC.2015.7063061
DO - 10.1109/ISSCC.2015.7063061
M3 - Conference contribution
AN - SCOPUS:84940733990
T3 - Digest of Technical Papers - IEEE International Solid-State Circuits Conference
SP - 332
EP - 333
BT - 2015 IEEE International Solid-State Circuits Conference, ISSCC 2015 - Digest of Technical Papers
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 62nd IEEE International Solid-State Circuits Conference, ISSCC 2015 - Digest of Technical Papers
Y2 - 22 February 2015 through 26 February 2015
ER -