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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Popis
Shrnutí:	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 underlying processes, signal analysis poses numerous challenges. Data-driven methods based on machine learning offer distinct solutions, but unfortunately the computations are not well supported by traditional DSP. This paper presents a custom processor that integrates a CPU with configurable accelerators for discriminative machine-learning functions. A support-vector-machine accelerator realizes various classification algorithms as well as various kernel functions and kernel formulations, enabling range of points within an accuracy-versus-energy and -memory trade space. An accelerator for embedded active learning enables prospective adaptation of the signal models by utilizing sensed data for patient-specific customization, while minimizing the effort from human experts. The prototype is implemented in 130-nm CMOS and operates from 1.2 V-0.55 V (0.7 V for SRAMs). Medical applications for EEG-based seizure detection and ECG-based cardiac-arrhythmia detection are demonstrated using clinical data, while consuming 273 μJ and 124 μJ per detection, respectively; this represents 62.4× and 144.7× energy reduction compared to an implementation based on the CPU. A patient-adaptive cardiac-arrhythmia detector is also demonstrated, reducing the analysis-effort required for model customization by 20 ×.
ISSN:	0018-9200 1558-173X
DOI:	10.1109/JSSC.2013.2253226