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適用於深度學習應用之高能源效率可重組仿神經形態運算晶片設計與製作
Shiue, Muh-Tian
(PI)
電機工程學系
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探索此專案觸及的研究主題。這些標籤是根據基礎獎勵/補助款而產生。共同形成了獨特的指紋。
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Keyphrases
Neuromorphic Computing
100%
Artificial Intelligence
100%
Reconfigurable
100%
Chip Design
100%
Deep Learning
100%
Chip Implementation
100%
Phonocardiogram
66%
Energy Efficiency
66%
RISC
33%
Evolution of Science
33%
Analog Implementation
33%
Neuromorphic
33%
Digital Circuit Implementation
33%
Computing Module
33%
Operation number
33%
Low-voltage Analog Circuits
33%
Neuromorphic Computing Architectures
33%
Neuron Cells
33%
Wearable Devices
33%
Automatic Speech Recognition
33%
Speech Processing
33%
Complicated System
33%
Monitoring Task
33%
High Energy
33%
Biomedical Signals
33%
Speech Recognition
33%
Technology Evolution
33%
Electronic Devices
33%
Object Recognition
33%
Digital Circuits
33%
Circuit Implementation
33%
Heart Disease
33%
Ultra-low Power
33%
Particle Swarm Optimization
33%
Neural Network
33%
Deep Neural Network
33%
Energy Consumption
33%
Computer Science
Deep Learning Method
100%
Digital Circuit
66%
Speech Recognition
66%
Energy Efficiency
66%
Speech Processing
33%
Emerging Technology
33%
Object Recognition
33%
Particle Swarm Optimization
33%
Computer Architecture
33%
Energy Consumption
33%
Wearable Device
33%
Analog Circuit
33%
Biomedical Signal
33%
Research Direction
33%
Artificial Intelligence
33%
Engineering
Deep Learning Method
100%
Energy Conservation
66%
Energy Efficiency
66%
Digital Electronics
66%
Speech Processing
33%
Particle Swarm Optimization
33%
Object Recognition
33%
Wearable Sensor
33%
Analog Circuit
33%
Automatic Speech Recognition
33%
Cell Model
33%
Artificial Intelligence
33%