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A low-power processor with configurable embedded machine-learning accelerators for high-order and adaptive analysis of medical-sensor signals
Kyong Ho Lee,
Naveen Verma
Electrical and Computer Engineering
Princeton Materials Institute
Research output
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Contribution to journal
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Article
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peer-review
153
Scopus citations
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Dive into the research topics of 'A low-power processor with configurable embedded machine-learning accelerators for high-order and adaptive analysis of medical-sensor signals'. Together they form a unique fingerprint.
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Keyphrases
Machine Learning Accelerator
100%
Embedded Machine Learning
100%
Adaptive Analysis
100%
Medical Information
50%
130 Nm CMOS
50%
Distinct Solutions
50%
Learning Function
50%
Sensed Data
50%
Model Customization
50%
Reconfigurable Accelerator
50%
Cardiac Arrhythmia Detection
50%
EEG-based Seizure Detection
50%
Clinical Data
50%
Embedded Actives
50%
Trade Space
50%
Patient-specific
50%
Clinical Value
50%
Low-power Sensors
50%
Kernel Function
50%
Engineering
Customisation
100%
Adaptive Analysis
100%
Signal Model
50%
Patient Specific
50%
Clinical Data
50%
Human Expert
50%
Classification Algorithm
50%
Computer Science
Classification Algorithm
33%
Seizure Detection
33%
Clinical Data
33%
Medical Application
33%
Medical Information
33%
Kernel Function
33%