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DL in IoT Edge

DeepLearning
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    • Physics in DL
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        • Save and Load
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        • Microgrid
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        • DLtrain in Jetson Nano
        • AI in Smart phone
        • Deploy AI in Sensors
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      • Share
  • Edge Infra
    • Compute via CUDA
      • Vector Add in CUDA
      • MAT Mul in CUDA
        • tst1.py
        • tst2.py
        • tst3.py
        • tst4.py
      • Reference
  • DL in IoT Edge
    • Jetson Nano
      • Setting Up
        • Resources
        • Installation
        • First Boot
        • JetPack
        • IO
        • Internet Access via Wi-Fi
        • RDP / SSH
        • git
        • nvcc
        • cmake 3.14
        • Python
      • Quick Demo
      • Hello World
      • Kernel Update
    • J7 App
      • Android SDK
      • Android NDK
      • J7 Source Code
      • Credential Update
      • Build
      • Generate APK
      • Install APK in Phone
      • Using J7
    • IBM Watson VR
      • Watson AI
      • Watson Studio
      • Watson VR service
      • Custom Model
      • Train
      • Test
      • Deploy
      • Client APP
      • Recap
    • IoTin5G
      • Part 1
      • Part 2
      • Part 3
    • Open Power
      • A2O
      • Open-CE
      • TL-V
    • Applications
      • Agriculture
      • Gesture Recognition
      • Nalla Paru
      • Ventilator
        • Biocontainment
        • Auscultation
        • Nurse
        • IoT Edge
DeepLearning
  • Home
    • Physics in DL
      • Boltzmann Machine
      • Digital Twin
    • jkEvents
      • PRIST University
      • IETEevent
      • Baranovichi
        • Model
          • TF Model
          • DLtrain Model
        • Data-Set
          • IBM Cloud Object Storage
          • MNIST++
        • Train DL Model
          • CoLab
          • TF On Prime CPU
          • Build DLtrain in Power 9
          • Build DLtrain for jetson Nano
          • Build DLtrain in Docker x86
          • DLtrain in Windows M/C
          • DLtrain On Prime CPU
        • Save and Load
        • Inference
          • Flask Micro Service
          • Inference via JavaScript
          • Deploy in Cloud
          • Android Phone
          • Jetson Nano TF Model
          • Jetson Nano DLtrain
          • Near Edge
          • DL Model Update
      • Sambhram
        • Digital Twin
        • Edge Ready
        • BO
        • Startups
      • CV Raman Global Univ
        • Thanks
        • Microgrid
        • AI in Monitoring MG
        • IoT sensors
        • Interpret Measurements
        • Edge in Microgrid
        • DLtrain in Jetson Nano
        • AI in Smart phone
        • Deploy AI in Sensors
        • Thank You
    • jkhome
      • Share
  • Edge Infra
    • Compute via CUDA
      • Vector Add in CUDA
      • MAT Mul in CUDA
        • tst1.py
        • tst2.py
        • tst3.py
        • tst4.py
      • Reference
  • DL in IoT Edge
    • Jetson Nano
      • Setting Up
        • Resources
        • Installation
        • First Boot
        • JetPack
        • IO
        • Internet Access via Wi-Fi
        • RDP / SSH
        • git
        • nvcc
        • cmake 3.14
        • Python
      • Quick Demo
      • Hello World
      • Kernel Update
    • J7 App
      • Android SDK
      • Android NDK
      • J7 Source Code
      • Credential Update
      • Build
      • Generate APK
      • Install APK in Phone
      • Using J7
    • IBM Watson VR
      • Watson AI
      • Watson Studio
      • Watson VR service
      • Custom Model
      • Train
      • Test
      • Deploy
      • Client APP
      • Recap
    • IoTin5G
      • Part 1
      • Part 2
      • Part 3
    • Open Power
      • A2O
      • Open-CE
      • TL-V
    • Applications
      • Agriculture
      • Gesture Recognition
      • Nalla Paru
      • Ventilator
        • Biocontainment
        • Auscultation
        • Nurse
        • IoT Edge
  • More
    • Home
      • Physics in DL
        • Boltzmann Machine
        • Digital Twin
      • jkEvents
        • PRIST University
        • IETEevent
        • Baranovichi
          • Model
            • TF Model
            • DLtrain Model
          • Data-Set
            • IBM Cloud Object Storage
            • MNIST++
          • Train DL Model
            • CoLab
            • TF On Prime CPU
            • Build DLtrain in Power 9
            • Build DLtrain for jetson Nano
            • Build DLtrain in Docker x86
            • DLtrain in Windows M/C
            • DLtrain On Prime CPU
          • Save and Load
          • Inference
            • Flask Micro Service
            • Inference via JavaScript
            • Deploy in Cloud
            • Android Phone
            • Jetson Nano TF Model
            • Jetson Nano DLtrain
            • Near Edge
            • DL Model Update
        • Sambhram
          • Digital Twin
          • Edge Ready
          • BO
          • Startups
        • CV Raman Global Univ
          • Thanks
          • Microgrid
          • AI in Monitoring MG
          • IoT sensors
          • Interpret Measurements
          • Edge in Microgrid
          • DLtrain in Jetson Nano
          • AI in Smart phone
          • Deploy AI in Sensors
          • Thank You
      • jkhome
        • Share
    • Edge Infra
      • Compute via CUDA
        • Vector Add in CUDA
        • MAT Mul in CUDA
          • tst1.py
          • tst2.py
          • tst3.py
          • tst4.py
        • Reference
    • DL in IoT Edge
      • Jetson Nano
        • Setting Up
          • Resources
          • Installation
          • First Boot
          • JetPack
          • IO
          • Internet Access via Wi-Fi
          • RDP / SSH
          • git
          • nvcc
          • cmake 3.14
          • Python
        • Quick Demo
        • Hello World
        • Kernel Update
      • J7 App
        • Android SDK
        • Android NDK
        • J7 Source Code
        • Credential Update
        • Build
        • Generate APK
        • Install APK in Phone
        • Using J7
      • IBM Watson VR
        • Watson AI
        • Watson Studio
        • Watson VR service
        • Custom Model
        • Train
        • Test
        • Deploy
        • Client APP
        • Recap
      • IoTin5G
        • Part 1
        • Part 2
        • Part 3
      • Open Power
        • A2O
        • Open-CE
        • TL-V
      • Applications
        • Agriculture
        • Gesture Recognition
        • Nalla Paru
        • Ventilator
          • Biocontainment
          • Auscultation
          • Nurse
          • IoT Edge
One Teacher able to handle not more than 40 students, But Power AC922 expected to handle 2000 plus CUDA cores and 100 plus Tensor Cores, when AI workload assigned to Power AC922.
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