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DeepLearning
  • Home
    • jkhome
      • Appa+Amma
      • jkIntro
      • Ph.D Thesis
      • Epigon Media Technologies
      • K.U. Leuven
      • education+work
      • Share
      • bus
    • jkEvents
      • SRM Chennai
        • p1
      • Presentation
      • NIT Trichy
      • IIT Roorkee
      • AI for Managers
        • toolset
        • AI Adoption
        • P1
        • About people
        • Ice Cream
        • Managers
      • Sri Sai Ram College
        • qr
      • Marwadi University FDP
        • qr
        • Problem Def
        • P1
        • P2
        • P3
        • P4
        • P5
        • P6
        • Eye
        • MCQ
        • DT v1
      • Winter Camp 2025
        • IoT Model
        • KG Students
        • Voice Teacher
      • S.A. Engineering College
        • AI ML in BUS
        • Ice Cream Venture
        • Session 2
        • Session 3
        • Session 4
        • Architecture
        • Requirements
      • TechnoJam
        • Phys2DS
      • IEEE Chennai
        • Ref
        • Abstract
        • Intro
        • MBSE Layer
        • weather Forecast
        • aiIoT
        • Pontryagin
        • Sensor classification
        • Sensor Fusion
        • Certify
        • mbse
        • Q1
        • Q2
        • Q2ans
        • Q1ans
        • Q1bans
        • Q3ans
        • Q4ans
        • Q5ans
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        • Q7ans
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        • Q11ans
        • Q10ans
        • Q9ans
        • Q8ans
        • Q16ans
      • PRIST University
      • Amrita Vishwa Vidyapeetham
      • IETEevent
      • Baranovichi
        • inference
          • jetsonnano-dltrain
      • Sambhram
        • BO
        • Startups
        • MBA induction Y22
      • CV Raman Global Univ
      • Dayananda Sagar
      • Nirma University -CPS Y22
        • Thanks to Nirma Univ
        • DL in Microgrid
        • AI in Security
        • Pontryagin Duality
        • Daily Inspection
        • Russell
        • IIOT4pointZero
        • Abstract
        • Test
      • PMU
      • IIT-Guwahati
        • Device Making
        • Power Modules
        • Testing Device and package
        • 48V cs 12V
        • CPU+GPU in car
        • mmWave Radar
        • Reference-iitg
      • IIT-Guwahati -L2
        • Physics and Computing
      • GITEX2022
      • NFSU Goa
      • RGUKT
      • IEEEevent
      • Christ University
        • talk1
        • ftbm
        • qr1
      • C-DAC Bangalore
      • Cambridge Institute of Technology
      • ADYPU
        • SE-Introduction
        • SE-Tool Set
          • SysML
        • Requirements
          • CubeSat
          • DOORS and CORE
        • Traceability Matrix
          • Trace Intro
          • Deep Learning Model
          • System Composer
        • Architecture
          • CubeSat Architecture
        • MODEL (MBSE)
          • Entropy
        • Design
          • DO 254
          • DO 178
        • Validation
          • Trace Bi Directions
          • Simulink
          • TASTE
          • NI LabView
          • Pontryagin
          • DOORs and CORE
        • Digital Thread
        • Deployment
          • Pre-Planning
          • Optimal System
          • Trace It
          • Your View
        • Reference
        • Students View
        • Assignment
        • Mock Test
      • Layola Institute of Technology
      • New Horizon
      • JSS Academy of Technical Education
      • Rotary Koramangala
      • KIOT
        • AIoT in Edge
      • Madras Institute of Technology
        • Interns
      • Bangalore Institute of Technology
      • TET-AI
      • AICSSYC
        • MBEinIoT
        • jkQR
        • Oct29Y24
      • PSNA
        • AI and IoT
        • CNN in FPGA
          • qr
    • For +2 Students
      • Data Science
        • Maths for Data Science
          • Week 1 M
          • Week 2 M
          • Week 3 M
          • Week 4 M
        • Statistics for Data Science
          • Week 1 S
          • Week 2 S
          • Week 3 S
          • Week 4 S
        • Computational Thinking
          • Week 1 C
          • Week 2 C
          • Week 3 C
          • Week 4 C
        • English
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          • Week 2 E
          • Week 3 E
          • Week 4 E
      • Physics
        • Modern Physics
        • Optics
        • Electricity and Magnetism
        • Thermal Physics
        • Mechanics
        • General
      • Maths
        • Integral calculus
        • Differential calculus
        • Vectors
        • Trigonometry
        • Probability
        • Matrices and Determinants
        • Binomial Theorem
        • Permutation and Combination
        • Logarithms
        • Sequence and Series
        • Quadratic Equations
        • Complex Numbers
      • Chemistry
        • Physical Chemistry
        • Organic Chemistry
        • Inorganic Chemistry
      • Utils
    • deep-learning
      • boltzmann-machine
  • DLtrain
    • Authors
    • Introduction
      • expert2DL
      • Layers
      • OnTable
      • GPT Model
      • BM
      • DLtrain
    • Mathematical Theory
      • Overview
      • Data to Networks
      • Discrete Probability
      • Bayesian Networks
      • Restricted Boltzmann machine
      • NN , CNN, RNN Model
      • Digital Twin
      • Large Model
        • Decoder
          • Next Token
          • Training
        • Language Model
        • Alex Model
        • Signal Processing
        • FDT 2
        • FDT 1
        • Flow Information
      • QIS
      • Deep Learning Networks
      • Theory of Learning
      • Unified Model
      • Uncertainty
    • Data Set
      • Data Source
      • Representation
      • TF support
      • PyTorch Support
      • DLtrain Native
      • Local Storage
      • Cloud Storage
    • Tool Set
      • Compute via CUDA
        • Setup Jetson Nano
        • Thread to Warp
        • Practice CUDA core
        • Orin
        • TP core
        • DLA
        • NICKEL
        • NGC
      • Docker
      • DLtrain
        • P6
        • P1
        • P2
        • P3
        • P4
        • P5
      • colab
      • DSP
        • pdf
        • Experiment 1
        • Music Synthesis
        • SDR
        • Doppler
        • AI in Signal Processing
        • Pontryagin
      • IC Design
        • Paul Mackerras
        • Yosys-FPGA
        • Microwatt
        • P0
        • Intro
        • Questions
        • Q1
        • Q2
        • Q3
        • Q4
        • Q5
        • Q6
        • Q7
        • Q8
        • Q9
        • Q10
        • Q11
        • Q12
        • Q13
        • Q14
        • Q15
        • Q16
        • Q17
        • Q18
        • Q19
        • Mixed Signal
      • apk
        • go Native
        • install Tool Set
        • Folder Structure
        • keystore
        • App Name
        • Build APK
        • Install APK
      • github Copilot Arm ext
    • Train DL Networks
      • DLtrain for DL
      • TensorFlow for DL
      • Save DL Networks
    • Deploy DL Networks
      • Load DL Networks
      • Cloud Native Service
        • CloudIntro
        • Deploy in Cloud
        • Inference via JavaScript
        • Flask Micro Service
      • Edge Native Service
        • J7 App
          • Architecture
          • Infra
          • Host
        • Kanshi
          • P1
          • P2
        • Near Edge
        • Jetson Nano DLtrain
        • Jetson Nano TF Model
    • Tutorial
      • Quick Review on skill
        • gf: AI Model
        • gf : IoT edge security
        • gf : train AI Model
        • gf: deploy AI in Edge
        • gf: Visual Recognition
        • gf: CPU for Auto
      • Deployment Engineer
      • Development Engineer
      • Design Engineer
      • Researcher
    • Silicon Vendors
      • DL Processor Trend
      • AMD FPGA
      • Nvidia
      • IBM Watson VR
        • Watson AI
        • Watson Studio
        • Watson VR service
        • Custom Model
        • vrTrain
        • vrTest
        • vrDeploy
        • vrClient APP
        • vrRecap
        • Power 9 Processor
    • Discussion
      • IoT sensors
      • Healthcare
        • Data set in Healthcare
        • Life Style
        • Inference Service
        • Clinical
        • Nurse
        • Audio Signal
        • Device
      • Agriculture
      • IoT in 5G
      • Recommendation
      • AUready
    • deploy-in-edge
      • kanshi
    • Publication
  • More
    • Home
      • jkhome
        • Appa+Amma
        • jkIntro
        • Ph.D Thesis
        • Epigon Media Technologies
        • K.U. Leuven
        • education+work
        • Share
        • bus
      • jkEvents
        • SRM Chennai
          • p1
        • Presentation
        • NIT Trichy
        • IIT Roorkee
        • AI for Managers
          • toolset
          • AI Adoption
          • P1
          • About people
          • Ice Cream
          • Managers
        • Sri Sai Ram College
          • qr
        • Marwadi University FDP
          • qr
          • Problem Def
          • P1
          • P2
          • P3
          • P4
          • P5
          • P6
          • Eye
          • MCQ
          • DT v1
        • Winter Camp 2025
          • IoT Model
          • KG Students
          • Voice Teacher
        • S.A. Engineering College
          • AI ML in BUS
          • Ice Cream Venture
          • Session 2
          • Session 3
          • Session 4
          • Architecture
          • Requirements
        • TechnoJam
          • Phys2DS
        • IEEE Chennai
          • Ref
          • Abstract
          • Intro
          • MBSE Layer
          • weather Forecast
          • aiIoT
          • Pontryagin
          • Sensor classification
          • Sensor Fusion
          • Certify
          • mbse
          • Q1
          • Q2
          • Q2ans
          • Q1ans
          • Q1bans
          • Q3ans
          • Q4ans
          • Q5ans
          • Q6ans
          • Q7ans
          • Q15ans
          • Q14ans
          • Q13ans
          • Q12ans
          • Q11ans
          • Q10ans
          • Q9ans
          • Q8ans
          • Q16ans
        • PRIST University
        • Amrita Vishwa Vidyapeetham
        • IETEevent
        • Baranovichi
          • inference
            • jetsonnano-dltrain
        • Sambhram
          • BO
          • Startups
          • MBA induction Y22
        • CV Raman Global Univ
        • Dayananda Sagar
        • Nirma University -CPS Y22
          • Thanks to Nirma Univ
          • DL in Microgrid
          • AI in Security
          • Pontryagin Duality
          • Daily Inspection
          • Russell
          • IIOT4pointZero
          • Abstract
          • Test
        • PMU
        • IIT-Guwahati
          • Device Making
          • Power Modules
          • Testing Device and package
          • 48V cs 12V
          • CPU+GPU in car
          • mmWave Radar
          • Reference-iitg
        • IIT-Guwahati -L2
          • Physics and Computing
        • GITEX2022
        • NFSU Goa
        • RGUKT
        • IEEEevent
        • Christ University
          • talk1
          • ftbm
          • qr1
        • C-DAC Bangalore
        • Cambridge Institute of Technology
        • ADYPU
          • SE-Introduction
          • SE-Tool Set
            • SysML
          • Requirements
            • CubeSat
            • DOORS and CORE
          • Traceability Matrix
            • Trace Intro
            • Deep Learning Model
            • System Composer
          • Architecture
            • CubeSat Architecture
          • MODEL (MBSE)
            • Entropy
          • Design
            • DO 254
            • DO 178
          • Validation
            • Trace Bi Directions
            • Simulink
            • TASTE
            • NI LabView
            • Pontryagin
            • DOORs and CORE
          • Digital Thread
          • Deployment
            • Pre-Planning
            • Optimal System
            • Trace It
            • Your View
          • Reference
          • Students View
          • Assignment
          • Mock Test
        • Layola Institute of Technology
        • New Horizon
        • JSS Academy of Technical Education
        • Rotary Koramangala
        • KIOT
          • AIoT in Edge
        • Madras Institute of Technology
          • Interns
        • Bangalore Institute of Technology
        • TET-AI
        • AICSSYC
          • MBEinIoT
          • jkQR
          • Oct29Y24
        • PSNA
          • AI and IoT
          • CNN in FPGA
            • qr
      • For +2 Students
        • Data Science
          • Maths for Data Science
            • Week 1 M
            • Week 2 M
            • Week 3 M
            • Week 4 M
          • Statistics for Data Science
            • Week 1 S
            • Week 2 S
            • Week 3 S
            • Week 4 S
          • Computational Thinking
            • Week 1 C
            • Week 2 C
            • Week 3 C
            • Week 4 C
          • English
            • Week 1 E
            • Week 2 E
            • Week 3 E
            • Week 4 E
        • Physics
          • Modern Physics
          • Optics
          • Electricity and Magnetism
          • Thermal Physics
          • Mechanics
          • General
        • Maths
          • Integral calculus
          • Differential calculus
          • Vectors
          • Trigonometry
          • Probability
          • Matrices and Determinants
          • Binomial Theorem
          • Permutation and Combination
          • Logarithms
          • Sequence and Series
          • Quadratic Equations
          • Complex Numbers
        • Chemistry
          • Physical Chemistry
          • Organic Chemistry
          • Inorganic Chemistry
        • Utils
      • deep-learning
        • boltzmann-machine
    • DLtrain
      • Authors
      • Introduction
        • expert2DL
        • Layers
        • OnTable
        • GPT Model
        • BM
        • DLtrain
      • Mathematical Theory
        • Overview
        • Data to Networks
        • Discrete Probability
        • Bayesian Networks
        • Restricted Boltzmann machine
        • NN , CNN, RNN Model
        • Digital Twin
        • Large Model
          • Decoder
            • Next Token
            • Training
          • Language Model
          • Alex Model
          • Signal Processing
          • FDT 2
          • FDT 1
          • Flow Information
        • QIS
        • Deep Learning Networks
        • Theory of Learning
        • Unified Model
        • Uncertainty
      • Data Set
        • Data Source
        • Representation
        • TF support
        • PyTorch Support
        • DLtrain Native
        • Local Storage
        • Cloud Storage
      • Tool Set
        • Compute via CUDA
          • Setup Jetson Nano
          • Thread to Warp
          • Practice CUDA core
          • Orin
          • TP core
          • DLA
          • NICKEL
          • NGC
        • Docker
        • DLtrain
          • P6
          • P1
          • P2
          • P3
          • P4
          • P5
        • colab
        • DSP
          • pdf
          • Experiment 1
          • Music Synthesis
          • SDR
          • Doppler
          • AI in Signal Processing
          • Pontryagin
        • IC Design
          • Paul Mackerras
          • Yosys-FPGA
          • Microwatt
          • P0
          • Intro
          • Questions
          • Q1
          • Q2
          • Q3
          • Q4
          • Q5
          • Q6
          • Q7
          • Q8
          • Q9
          • Q10
          • Q11
          • Q12
          • Q13
          • Q14
          • Q15
          • Q16
          • Q17
          • Q18
          • Q19
          • Mixed Signal
        • apk
          • go Native
          • install Tool Set
          • Folder Structure
          • keystore
          • App Name
          • Build APK
          • Install APK
        • github Copilot Arm ext
      • Train DL Networks
        • DLtrain for DL
        • TensorFlow for DL
        • Save DL Networks
      • Deploy DL Networks
        • Load DL Networks
        • Cloud Native Service
          • CloudIntro
          • Deploy in Cloud
          • Inference via JavaScript
          • Flask Micro Service
        • Edge Native Service
          • J7 App
            • Architecture
            • Infra
            • Host
          • Kanshi
            • P1
            • P2
          • Near Edge
          • Jetson Nano DLtrain
          • Jetson Nano TF Model
      • Tutorial
        • Quick Review on skill
          • gf: AI Model
          • gf : IoT edge security
          • gf : train AI Model
          • gf: deploy AI in Edge
          • gf: Visual Recognition
          • gf: CPU for Auto
        • Deployment Engineer
        • Development Engineer
        • Design Engineer
        • Researcher
      • Silicon Vendors
        • DL Processor Trend
        • AMD FPGA
        • Nvidia
        • IBM Watson VR
          • Watson AI
          • Watson Studio
          • Watson VR service
          • Custom Model
          • vrTrain
          • vrTest
          • vrDeploy
          • vrClient APP
          • vrRecap
          • Power 9 Processor
      • Discussion
        • IoT sensors
        • Healthcare
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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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