Presentation about Klepsydra technical and business details

2. Context

3. Spin-off of Alerion Technologies

4. Error fixing costs
• Cost of fixing
software errors
grows
exponentially with
time.
• In the case of
robotics software
the curve is even
more steep.

5. Multiple Middleware

6. Embedded Image processing

7. Our Solution

8. Concept
• Transparent to
applications
• No connection
to the
middleware from
application
Klepsydra Abstraction Layer
Application 1 Application 2

9. Innovation of Klepsydra

10. Result
• Klepsydra
‘absorbs’ and
delivers all
messages from
middleware.
• Performance
can significantly
increase
Klepsydra Abstraction Layer
Application 1 Application 2
Blocking queues
Disruptor

11. One step further: unit testing
framework
Mock Middleware
Klepsydra Abstraction Layer
Application 1

12. Code Generation Tool
• Generates POCOs (Plan Old
C++ Object, which are
classes with no
dependencies to any library)
to be used in Klepsydra
• Generates the mapping
classes between Klepsydra
and all middleware (ROS,
DDS, ZMQ)

13. Performance Monitoring
Tools
Klepsydra Abstraction Layer
Application 1 Application 2
Telemetry UI
Statistics Monitoring
of publishers,
subscribers and
services
REST API
Monitoring

14. How does it work

15. The Application API
• Typical async API:
• publish, registerListener, etc.
• Plus some ‘sugar’
• registerOnce, callback, etc
• And some performance
• publishNoCopy, Process and forward, etc.
Klepsydra Abstraction Layer
Application 1

16. Assembling the Application
Kl. Abs. Lay.Klepsydra Abstraction Layer
Application 1 Application 2 Application 4Application 3
Blocking queues
Kl. Abs. Lay.
Disruptor

17. The assembly API
• Creation wizard for
• eventloop, blocking queues, disruptor
• Including performance param like object pool, etc.
• Connection wizard to middleware:
• ROS, DDS, ZMQ
• Including performance param like object pool, etc.
• Also, assembly with event loop, blocking queues and disruptor

18. The benefits

19. •Faster and higher resolution image processing.
•Requirements for hardware are less demanding
as resources are optimised.
•Hardware costs are then reduced as well.
•Longer life of satellites and space robots
1. Performance
On-board
Hardware resources
High performance
tuning
&
Image processing
module
+ =
On-board
Hardware resources

20. Example
Before Klepsydra:
Low resolution camera
After Klepsydra:
high resolution camera
Low
resource consumption
High resource consumption

21. Performance Monitoring in
Real Time

22. • The code is thoroughly tested and has higher
quality.
• Less maintenance costs and
• Safer and reliable continuous deployments.
2. Testing and CI
Source code
Target

23. Example
• Real example of
Gamesa/Siemens
Drones
• 4 Testing Phases
• With this approach,
more than 95% of
bugs are found
before reaching
production.
1. Unit Tests 2. Integration Tests
3. Simulation 4. Real scenario Tests

24. Conclusions
The adoption of Klepsydra as development
framework for embedded software leads not only
to high performance and quality, but more
importantly to system predictability which a key
feature in robotics and aerospace sectors.
High performance
Predictablity
+

25. The Market

26. Market of complex embedded software
Image processing
High performance Application Integration
Machine Learning

27. Sectors Targeted by Klepsydra
Aerospace
Autonomous Cars
Defence
Drones
Robotics

28. Licensing Model

29. Product Licensing Model
The business model is 'Freemium' based:
• Free version: free for prototype and non-
commercial development, research centres and
universities.
• Reduced pricing model for startups.
• Commercial version: development license
based on number of developer stations.

30. Klepsydra Future

31. Future features
Distributed
computing
Telemetry UI
Klepsydra Core API
Cloud Computing
Administration
REST API
Real-time
OSBinding to
High performance
Middleware
Basic Middleware Image processing
performance module

32. Thanks! Q & A