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Hans Salomonsson
CEO, Embedl
Stockholm Tech Live
12. May 2022
VEDLIoT – Very Efficient
Deep Learning in IoT
2
 Platform
 Hardware: Scalable, heterogeneous, distributed
 Accelerators: Efficiency boost by FPGA and ASIC technology
 Toolchain: Optimizing Deep Learning for IoT (Embedl)
 Use cases
 Industrial IoT
 Automotive
 Smart Home
 Open call
 At project mid-term
 Early use and evaluation of VEDLIoT technology
Very Efficient Deep Learning for IoT – VEDLIoT
 Call: H2020-ICT2020-1
 Topic: ICT-56-2020 Next Generation Internet of Things
 Duration: 1. November 2020 – 31. Oktober 2023
 Coordinator: Bielefeld University (Germany)
 Overall budget: 7 996 646.25 €
 Consortium: 12 partners from 4 EU countries (Germany,
Poland, Portugal and Sweden) and one associated
country (Switzerland).
More info:
 https://www.vedliot.eu/
 https://twitter.com/VEDLIoT
 https://www.linkedin.com/company/vedliot/
3
 Bielefeld University (UNIBI) - Coordinator
 Christmann (CHR)
 University of Osnabrück (UOS)
 Siemens (SIEMENS)
 University of Neuchâtel (UNINE)
 University of Lisbon (FC.ID)
 Chalmers (CHALMERS)
 University of Gothenburg (UGOT)
 RISE (RISE)
 EmbeDL (EMBEDL)
 Veoneer (VEONEER)
 Antmicro (ANT)
VEDLIOT partners
4
Cognitive IoT Platforms
Security,
Privacy
and
Trust,
Robustness
and
Safety
Hardware Accelerators
Requirements
Use Cases
Industrial IoT Smart Home
Automotive Open Call
Toolchain
VEDLIoT structure
10x performance
improvement
10x efficiency
improvement
Reconfigurable,
heterogeneous,
scalable
6
VEDLIoT Hardware Platform
 Heterogeneous, modular, scalable microserver system
 Supporting the full spectrum of IoT from embedded over the edge towards the cloud
 Different technology concepts for improving
x86
GPU
ML-ASIC
ARM v8
GPU
SoC
FPGA
SoC
RISC-V
FPGA
VEDLIOT Cognitive
IoT Platform
 Performance
 Cost-effectiveness
 Maintainability
 Reliability
 Energy-Efficiency
 Safety
13
Microserver overview
t.RECS
RECS|Box
u.RECS
16
Flexible and adaptable Accelerators for Deep Learning
DL
Model
DL Model
CPU, GPU-
SoC,
ML-SoC
FPGA-SoC
 End of Moore’s law & dark silicon
=> Domain Specific Architectures (DSA)
 Efficient, flexible, scalable accelerators for
the compute continuum
 Algotecture
 Optimized DL algorithms
 Optimized toolchain
 Optimized computer architecture
Heterogeneous DL
Accelerator
Algotecture/
Co-Designed DL
Accelerator
Compiler
Co-Design
18
VEDLIoT‘s Deep Learning Toolchain
Enabling the rapid convergence of the fast pace
innovation on the hardware and software
Frameworks &
Exchange Formats
Optimization Engine Compilers &
Runtime APIs
Heterogeneous
Hardware
Platforms
19
 Increase safety, health and well being of residents – acceleration of AI methods for
demand-oriented user-home interaction
Use case: Smart Home / Assisted Living
21
 Face recognition
 mobilenetSSD trained on WIDERFACE dataset
 Object detection
 YoloV3, Efficient-Net, yoloV4-tiny
 Gesture detection
 YoloV4-tiny with 3 Yolo layers (usually: 2 layers)
 Speech recognition
 Mozilla DeepSpeech
 AI Art: Style-Gan trained on works of arts
 Collect usage data in situation memory
Smart Mirror – Neural Networks
22
Industrial IoT – drive condition classification
 Control applications need DL-based condition classification
 On the edge device for low power consumption
 Suggestions for control and maintenance
 DL methods on all communication layers
 DL in a distributed architecture
 Dynamically configured systems
 Sensored testbench with 2 motors
 Acceleration, Magnetic field, Temperature,
IR-Cam (temperature), Current-Sensors, Torque
 On / Off detection without
motor current or voltage
 Cooling fault detection
 Bearing fault detection
23
Industrial IoT – Arc detection for DC distributions
 AI based pattern recognition for different local sensor data
 current, magnetic field, vibration, temperature, low resolution infrared picture
 Safety critical nature
 response time should be <10ms
 AI based or AI supported decision made by the sensor node itself or by a local part of the sensor
network
24
 Improve performance/cost ratio – AI processing hardware
distributed over the entire chain
Use case: Automotive
25
The fun has just started!
Follow our work!
https://twitter.com/VEDLIoT
https://www.linkedin.com/company/vedliot/
https://vedliot.eu
Be part of it
Open call at project mid-term
Allow early use and evaluation of VEDLIoT
technology
26
Thank you for your
attention.
Contact
Hans Salomonsson
Embedl, Sweden
hans@embedl.ai
27
End-to-end attestation, support for execution and communication
 Support for
 End-to-end trust (attestation)
 Secrets distribution (keys)
 Machine learning (federated, streaming, …)
 Requirements
 Support for edge applications
 Decentralised, dynamic, loosely organised
 Rely on
 Trusted execution environments
 BFT and peer-to-peer algorithms
 Blockchain concepts
VEDLIoT
app
Attestation
Pool
28
Simulation platform for IoT
 Open source framework for software/hardware co-development with CI-driven testing
capabilities, as well as metrics for measuring efficiency of ML workloads
 Enables development and continuous testing of VEDLIoT’s security features and its robustness
 Renode is available to all project members and future users of VEDLIoT and will include a
simulated model of the RISC-V-based FPGA SoC platform developed as part of the VEDLIoT
project
29
Safety and robustness
 Define monitors for timeliness and data quality assessment
 Develop safety argumentation for adaptive solutions,
exploiting architectural hybridization
Hybrid System architecture:
• Some system components implemented
with assured reliability (as needed)
• Remaining components subject to
uncertainties and prone to failures
Safety requirements:
• Defined at design time for each operational
mode
Monitoring data:
• Collected in run-time, to allow verifying if
safety requirements are being satisfied
and trigger reconfiguration if not
Timeliness (e.g., deadline
miss detection)
Sensor data quality (e.g.,
outlier, noise detection)
Accuracy of DL systems
(e.g., anomaly detection)

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VEDLIoT at Stockholm Tech Live 2022

  • 1. Hans Salomonsson CEO, Embedl Stockholm Tech Live 12. May 2022 VEDLIoT – Very Efficient Deep Learning in IoT
  • 2. 2  Platform  Hardware: Scalable, heterogeneous, distributed  Accelerators: Efficiency boost by FPGA and ASIC technology  Toolchain: Optimizing Deep Learning for IoT (Embedl)  Use cases  Industrial IoT  Automotive  Smart Home  Open call  At project mid-term  Early use and evaluation of VEDLIoT technology Very Efficient Deep Learning for IoT – VEDLIoT  Call: H2020-ICT2020-1  Topic: ICT-56-2020 Next Generation Internet of Things  Duration: 1. November 2020 – 31. Oktober 2023  Coordinator: Bielefeld University (Germany)  Overall budget: 7 996 646.25 €  Consortium: 12 partners from 4 EU countries (Germany, Poland, Portugal and Sweden) and one associated country (Switzerland). More info:  https://www.vedliot.eu/  https://twitter.com/VEDLIoT  https://www.linkedin.com/company/vedliot/
  • 3. 3  Bielefeld University (UNIBI) - Coordinator  Christmann (CHR)  University of Osnabrück (UOS)  Siemens (SIEMENS)  University of Neuchâtel (UNINE)  University of Lisbon (FC.ID)  Chalmers (CHALMERS)  University of Gothenburg (UGOT)  RISE (RISE)  EmbeDL (EMBEDL)  Veoneer (VEONEER)  Antmicro (ANT) VEDLIOT partners
  • 4. 4 Cognitive IoT Platforms Security, Privacy and Trust, Robustness and Safety Hardware Accelerators Requirements Use Cases Industrial IoT Smart Home Automotive Open Call Toolchain VEDLIoT structure 10x performance improvement 10x efficiency improvement Reconfigurable, heterogeneous, scalable
  • 5. 6 VEDLIoT Hardware Platform  Heterogeneous, modular, scalable microserver system  Supporting the full spectrum of IoT from embedded over the edge towards the cloud  Different technology concepts for improving x86 GPU ML-ASIC ARM v8 GPU SoC FPGA SoC RISC-V FPGA VEDLIOT Cognitive IoT Platform  Performance  Cost-effectiveness  Maintainability  Reliability  Energy-Efficiency  Safety
  • 7. 16 Flexible and adaptable Accelerators for Deep Learning DL Model DL Model CPU, GPU- SoC, ML-SoC FPGA-SoC  End of Moore’s law & dark silicon => Domain Specific Architectures (DSA)  Efficient, flexible, scalable accelerators for the compute continuum  Algotecture  Optimized DL algorithms  Optimized toolchain  Optimized computer architecture Heterogeneous DL Accelerator Algotecture/ Co-Designed DL Accelerator Compiler Co-Design
  • 8. 18 VEDLIoT‘s Deep Learning Toolchain Enabling the rapid convergence of the fast pace innovation on the hardware and software Frameworks & Exchange Formats Optimization Engine Compilers & Runtime APIs Heterogeneous Hardware Platforms
  • 9. 19  Increase safety, health and well being of residents – acceleration of AI methods for demand-oriented user-home interaction Use case: Smart Home / Assisted Living
  • 10. 21  Face recognition  mobilenetSSD trained on WIDERFACE dataset  Object detection  YoloV3, Efficient-Net, yoloV4-tiny  Gesture detection  YoloV4-tiny with 3 Yolo layers (usually: 2 layers)  Speech recognition  Mozilla DeepSpeech  AI Art: Style-Gan trained on works of arts  Collect usage data in situation memory Smart Mirror – Neural Networks
  • 11. 22 Industrial IoT – drive condition classification  Control applications need DL-based condition classification  On the edge device for low power consumption  Suggestions for control and maintenance  DL methods on all communication layers  DL in a distributed architecture  Dynamically configured systems  Sensored testbench with 2 motors  Acceleration, Magnetic field, Temperature, IR-Cam (temperature), Current-Sensors, Torque  On / Off detection without motor current or voltage  Cooling fault detection  Bearing fault detection
  • 12. 23 Industrial IoT – Arc detection for DC distributions  AI based pattern recognition for different local sensor data  current, magnetic field, vibration, temperature, low resolution infrared picture  Safety critical nature  response time should be <10ms  AI based or AI supported decision made by the sensor node itself or by a local part of the sensor network
  • 13. 24  Improve performance/cost ratio – AI processing hardware distributed over the entire chain Use case: Automotive
  • 14. 25 The fun has just started! Follow our work! https://twitter.com/VEDLIoT https://www.linkedin.com/company/vedliot/ https://vedliot.eu Be part of it Open call at project mid-term Allow early use and evaluation of VEDLIoT technology
  • 15. 26 Thank you for your attention. Contact Hans Salomonsson Embedl, Sweden hans@embedl.ai
  • 16. 27 End-to-end attestation, support for execution and communication  Support for  End-to-end trust (attestation)  Secrets distribution (keys)  Machine learning (federated, streaming, …)  Requirements  Support for edge applications  Decentralised, dynamic, loosely organised  Rely on  Trusted execution environments  BFT and peer-to-peer algorithms  Blockchain concepts VEDLIoT app Attestation Pool
  • 17. 28 Simulation platform for IoT  Open source framework for software/hardware co-development with CI-driven testing capabilities, as well as metrics for measuring efficiency of ML workloads  Enables development and continuous testing of VEDLIoT’s security features and its robustness  Renode is available to all project members and future users of VEDLIoT and will include a simulated model of the RISC-V-based FPGA SoC platform developed as part of the VEDLIoT project
  • 18. 29 Safety and robustness  Define monitors for timeliness and data quality assessment  Develop safety argumentation for adaptive solutions, exploiting architectural hybridization Hybrid System architecture: • Some system components implemented with assured reliability (as needed) • Remaining components subject to uncertainties and prone to failures Safety requirements: • Defined at design time for each operational mode Monitoring data: • Collected in run-time, to allow verifying if safety requirements are being satisfied and trigger reconfiguration if not Timeliness (e.g., deadline miss detection) Sensor data quality (e.g., outlier, noise detection) Accuracy of DL systems (e.g., anomaly detection)