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CASE: CNN Acceleration for Speech-Classification in Edge-Computing
Haris Gulzar
, Muhammad Shakeel
, Kenji Nishida
, Katsutoshi Itoyama
, Kazuhiro Nakadai
, Hideharu Amano
情報工学科
研究成果
:
Conference contribution
2
リンクは新しいタブで開きます
被引用数 (Scopus)
概要
フィンガープリント
フィンガープリント
「CASE: CNN Acceleration for Speech-Classification in Edge-Computing」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。
並べ替え順
重み付け
アルファベット順
Keyphrases
Convolutional Neural Network
100%
Edge Computing
100%
Convolutional Neural Network Accelerator
100%
Speech Classification
100%
Number of Parameters
66%
System-on-chip
66%
Operation number
66%
IoT Devices
66%
Edge Computing Devices
66%
High Performance
33%
Daily Life
33%
Computational Cost
33%
Application-oriented
33%
Computationally Expensive
33%
Audio Signal
33%
Robust Solution
33%
Hardware Accelerator
33%
Speech Commands
33%
Cloud Server
33%
Machine Learning Algorithms
33%
Validation Accuracy
33%
AI Applications
33%
Efficient Extraction
33%
Neural Network Performance
33%
Lightweight Model
33%
Speech Command Classification
33%
Speech Recognition Engine
33%
Computational Latency
33%
Inference Latency
33%
Scalable Solutions
33%
State-of-the-art Performance
33%
Edge Server
33%
Speech Interface
33%
Low-Power Edge Device
33%
Low-power IoT
33%
Computer Science
Edge Computing
100%
Convolutional Neural Network
100%
Internet-Of-Things
60%
Computing Device
40%
Network Performance
20%
Computational Cost
20%
Performing Network
20%
Speech Recognition
20%
Hardware Accelerator
20%
Art Performance
20%
Machine Learning Algorithm
20%
Edge Server
20%
Artificial Intelligence
20%
Field Programmable Gate Array
20%