A Low-Power Processor With Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals

Low-power sensing technologies have emerged for acquiring physiologically indicative patient signals. However, to enable devices with high clinical value, a critical requirement is the ability to analyze the signals to extract specific medical information. Yet given the complexities of the underlyin...

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Vydané v:	IEEE journal of solid-state circuits Ročník 48; číslo 7; s. 1625 - 1637
Hlavní autori:	Kyong Ho Lee, Verma, N.
Médium:	Journal Article Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.07.2013 Institute of Electrical and Electronics Engineers
Predmet:	Active learning (subject-specific adaptation) Adaptation models Applied sciences biomedical electronics Brain models Computational modeling Data models Design. Technologies. Operation analysis. Testing Electronics Exact sciences and technology General equipment and techniques Instruments, apparatus, components and techniques common to several branches of physics and astronomy Integrated circuits Integrated circuits by function (including memories and processors) Kernel machine learning (artificial intelligence) medical signal processing Physics Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing support vector machine (SVM) Support vector machines Static random access memory Prototype Random access memory support vector machine (SVM) Information extraction Support vector machine Implementation Learning Prospective Complementary MOS technology machine learning (artificial intelligence) Medical application Discriminant analysis Measurement sensor Active learning (subject-specific adaptation) Algorithm Kernel method medical signal processing Learning (artificial intelligence) Integrated circuit Signal processing Low-power electronics Digital signal processor Signal analysis Artificial intelligence Biomedical electronics
ISSN:	0018-9200, 1558-173X
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