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

• Published in: IEEE journal of solid-state circuits Vol. 48; no. 7; pp. 1625 - 1637
• Main Authors: Kyong Ho Lee, Verma, N.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01.07.2013; Institute of Electrical and Electronics Engineers
• Subjects: 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

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...