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Genetic Programming for Energy-Efficient and Energy-Scalable Approximate Feature Computation in Embedded Inference Systems
Jie Lu
, Hongyang Jia
,
Naveen Verma
,
Niraj K. Jha
Electrical and Computer Engineering
Princeton Materials Institute
Princeton Language and Intelligence (PLI)
Research output
:
Contribution to journal
›
Article
›
peer-review
12
Scopus citations
Overview
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Dive into the research topics of 'Genetic Programming for Energy-Efficient and Energy-Scalable Approximate Feature Computation in Embedded Inference Systems'. Together they form a unique fingerprint.
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Keyphrases
Approximate Computing
16%
Arrhythmia Detection
16%
Baseline Features
33%
Baseline Performance
16%
Bit Precision
16%
Electroencephalogram
16%
Embedded Inference
100%
Embedded Platform
16%
Energy Modeling
16%
Feature Computation
100%
Feature Data
16%
Genetic Programming
100%
Genetic Programming Model
33%
Inference System
100%
Low-power Microprocessor
16%
Number of False Alarms
16%
Precision Scaling
16%
Primitive Function
50%
Seizure Detection
33%
Sensitivity Specificity
16%
Subtraction by Addition
16%
Variable Energy
16%
Voltage Limit
16%
Computer Science
Approximate Computing
16%
Embedded Platform
16%
Genetic Programming
100%
Inference System
100%
Engineering
Bit Precision
12%
Energy Constraint
12%
Energy Modeling
12%