A novel approach to Artificial Intelligence On-Board New generations of spacecrafts are required to perform tasks with an increased level of autonomy. Space exploration, Earth Observation, space robotics, etc. are all growing fields in Space that require more sensors and more computational power to perform these missions. Sensors, embedded processors, and hardware in general have hugely evolved in the last decade, equipping embedded systems with large number of sensors that will produce data at rates that has not been seen before while simultaneously having computing power capable of large data processing on-board. Near-future spacecrafts will be equipped with large number of sensors that will produce data at high-speed rates in space and data processing power will be significantly increased. Future missions such as Active Debris Removal will rely on novel high-performance avionics to support image processing and Artificial Intelligence algorithms with large workloads. Similar requirements come from Earth Observation applications, where data processing on-board can be critical in order to provide real-time reliable information to Earth. This new scenario has brought new challenges with it: low determinism, excessive power needs, data losses and large response latency. In this project, Klepsydra AI is used as a novel approach to on-board artificial intelligence. It provides a very sophisticated threading model combination of pipeline and parallelization techniques applied to deep neural networks, making AI applications much more efficient and reliable. This new approach has been validated with several DNN models and two different computer architectures. The results show that the data processing rate and power saving of the applications increase substantially with respect to standard AI solutions.

1. ADCSS PRESENTATION
26.10.2022
pablo.ghiglino@klepsydra.com
www.klepsydra.com
Klepsydra Technologies

2. Part 1
Klepsydra
Technology

3. Part 2.1
Lock-free
programming

4. CONTEXT: PARALLEL PROCESSING
• Recurrent mission
failures due to
software
• Access to sensor data
from Earth is time
consuming.
• Satellites struggle to
meet power
requirements
Consequences for Space applications
Challenges on on-board processing
CPU
Usage
Low Medium
Data volume
Modern hardware and old
software:
• Computers max out with low to
medium data volumes
• Inef
fi
cient use of resources
• Excessive power for low data
processing

5. COMPARE AND SWAP
• Compare-and-swap (CAS) is an instruction
used in multithreading to achieve
synchronisation. It compares the contents of
a memory location with a given value and,
only if they are the same, modi
fi
es the
contents of that memory location to a new
given value. This is done as a single
atomic operation.
• Compare-and-Swap has been an integral
part of the IBM 370 architectures since
1970.
• Maurice Herlihy (1991) proved that CAS can
implement more of these algorithms than
atomic read, write, and fetch-and-add

6. LOCK BASED PARALLELISATION VS
LOCK FREE PARALLELISATION
• Threads need to acquire lock to access resource.
• Context switch:
• Suspended while resource is locked by
someone else
• Awaken when resource is available.
• Not deterministic, power consuming context switch.
• Threads access resources using ‘Atomic Operations’
• Compare and Swap (CAS):
• Try to update a memory entry
• If not possible tried again
• No locks involved, but ‘busy wait’
• No context switch required.

7. PROS AND CONS OF LOCK-FREE
PROGRAMMING
CPU
Usage
Data volume
CPU
Usage
Data volume
Lock-free programming
Pros:
• Less CPU consumption required
• Lower latency and higher data throughput
• Substantial increase in determinism
Cons:
• Extremely dif
fi
cult programming
technique
• Requires processor with CAS instructions
(90% of the market have them, though)

8. Part 2.2
Klepsydra
SDK

9. Lightweight, modular and compatible with most used operating systems
Worldwide
application
Klepsydra
SDK
Klepsydra
GPU
Streaming
Klepsydra
AI
Klepsydra
ROS2
executor
plugin
SDK – Software Development Kit
Boost data processing at the edge for general
applications and processor intensive
algorithms
AI – Artificial Intelligence
High performance deep neural network
(DNN) engine to deploy any AI or
machine learning module at the edge
ROS2 Executor plugin
Executor for ROS2 able to process up
to 10 times more data with up to 50%
reduction in CPU consumption.
GPU (Graphic Processing Unit)
High parallelisation of GPU to increase
the processing data rate and GPU
utilization
THE PRODUCT

10. KLEPSYDRA SDK OVERVIEW
SDK CORE
Plugins: ROS,
ROS2, OpenCV,
CANopen
Code
generator
Performance
analysis
Language bindings
Applications
Basic features
Advanced features

11. Event Loop
Sensor Multiplexer
Two main data
processing approaches
Producer 1
Consumer 1 Consumer 2
Producer 2
Producer 3
Consumer
Producer 1
11

12. Part 2.3
Algorithms in
Klepsydra
SDK

13. LOCK-FREE AS ALTERNATIVE TO
PARALLELISATION
Parallelisation Pipeline

14. APPROACH
Input
Matrix
B = A x A C = B x B
Output
Matrix
Input
Matrix B = A x A
Output
Matrix
C = B x B
Klepsydra Parallel Streaming Setup
OpenMP Sequential Setup
{
Thread 1
{
Thread 2
{
Parallelised
{
Parallelised

15. BENCHMARK DESCRIPTION
Description
• Given an input matrix, a number of sequential multiplications will be
performed:
• Step 1: A => B = A x A => Step 2 : C = B x B…
• Matrix A randomly generated on each new sequence
Parameters:
• Matrix dimensions: 100x100
• Data type: Float, integer
• Number of multiplications per matrix: [10, 60]
• Processing frequency: [2Hz - 100Hz]
Technical Spec
• Computer: Odroid XU4
• OS: Ubuntu 18.04

16. FLOAT PERFORMANCE RESULTS II
CPU Usage. 30 Steps
0,0
20,0
40,0
60,0
80,0
Publishing Rate (Hz)
2,00 6,50 11,00 15,50 20,00
OpenMp Klepsydra
Throughput. 30 Steps
0,00
5,00
10,00
15,00
20,00
Publishing Rate (Hz)
2,00 6,50 11,00 15,50 20,00
OpenMp Klepsydra
CPU Usage. 40 Steps
0,0
17,5
35,0
52,5
70,0
Publishing Rate (Hz)
2,00 5,00 8,00 11,00 14,00
OpenMp Klepsydra
Throughput. 40 Steps
0,00
3,50
7,00
10,50
14,00
Publishing Rate (Hz)
2,00 5,00 8,00 11,00 14,00
OpenMp Klepsydra
Latency. 40 Steps
0,00
60,00
120,00
180,00
240,00
Publishing Rate (Hz)
2,00 5,00 8,00 11,00 14,00
OpenMp Klepsydra
Latency. 30 Steps
0,00
45,00
90,00
135,00
180,00
Publishing Rate (Hz)
2,00 6,50 11,00 15,50 20,00
OpenMp Klepsydra

17. Part 2.4
Klepsydra AI

18. 2-DIM THREADING MODEL
Input
Data
Layer
Output
Data
First dimension: pipelining
{
Thread 1 (Core 1)
Layer
Layer
Layer
Layer
Layer
{
Thread 2 (Core 2)
Layer
Layer
Layer
Layer
Layer Layer Layer Layer Layer
Deep Neural Network Structure

19. 2-DIM THREADING MODEL
Input
Data
Output
Data
Second dimension: Matrix
multiplication parallelisation
{
T
hread
1
(Core
1)
Layer
{
T
hread
2
(Core
2)
{
T
hread
3
(Core
3)

20. 2-DIM THREADING MODEL
Core 1 Core 2
Core 3 Core 4
Layer
Layer
Layer
Layer
Layer
Layer
Core 1 Core 2
Core 3 Core 4
Layer
Layer
Layer
Layer
Layer
Layer
Core 1 Core 2
Core 3 Core 4
Layer
Layer
Layer
Layer
Layer
Layer
Layer
Layer
Layer
• Low CPU
• High throughput CPU
• High latency
• Mid CPU
• Mid throughput CPU
• Mid latency
• High CPU
• Mid throughput CPU
• Low latency
Threading model con
fi
guration

21. 21
Example of performance benchmarks
Klepsydra AI optimised for Latency vs CPU
CPU
Usage
0
30
60
90
Data Rate (Hz)
0 10 20 30
Latency Optimisation CPU Optimisaion TF Lite
Latency = 29ms
Low CPU Saturation
Latency = 97ms
TF Lite Saturation
Latency = 120ms

22. Part 2.4
The auto-tuning
software

23. KLEPSYDRA SDO PROCESS
• Klepsydra Streaming Optimiser (KSO):
• Runs on a separate computer
• Executes several dry runs on the OBC
• Collect statistics
• Runs a genetic algorithm to
fi
nd the optimal
solution for latency, power or throughput
• The main variable to optimise is the distribution of
layers on the HPDP cluster

24. KLEPSYDRA STREAMING DISTRIBUTION
OPTIMISER (SDO)

25. ONNX API
class KPSR_API OnnxDNNImporter
{
public:
/**
* @brief import an onnx file and uses a default eventloop factory for all processor cores
* @param onnxFileName
* @param testDNN
* @return a share pointer to a DeepNeuralNetwork object
*
* When log level is debug, dumps the YAML configuration of the default factory.
* It makes use of all processor cores.
*/
static std::shared_ptr<kpsr::ai::DeepNeuralNetworkFactory> createDNNFactory(const std::string & onnxFileName,
bool testDNN = false);
/**
* @brief importForTest an onnx file and uses a default synchronous factory
* @param onnxFileName
* @param envFileName. Klepsydra AI configuration environment file.
* @return a share pointer to a DeepNeuralNetwork object
*
* This method is intented to be used for testing purposes only.
*
*/
static std::shared_ptr<kpsr::ai::DeepNeuralNetworkFactory> createDNNFactory(const std::string & onnxFileName,
const std::string & envFileName);
};
25

26. KPSR File API
class KPSR_API DNNImporter
{
public:
/**
* @brief import a klepsydra model file and uses a default eventloop factory for all processor cores
* @param modelFileName
* @param testDNN
* @return a shared pointer to a DeepNeuralNetwork object
*
* When log level is debug, dumps the YAML configuration of the default factory.
* It makes use of all processor cores.
*/
static std::shared_ptr<kpsr::ai::DeepNeuralNetworkFactory> createDNNFactory(const std::string & modelFileName,
bool testDNN = false);
/**
* @brief importForTest a klepsydra model file and uses a default synchronous factory
* @param modelFileName
* @param envFileName. Klepsydra AI configuration environment file.
* @return a shared pointer to a DeepNeuralNetwork object
*
* This method is intended to be used for testing purposes only.
*/
static std::shared_ptr<kpsr::ai::DeepNeuralNetworkFactory> createDNNFactory(const std::string & modelFileName,
const std::string & envFileName);
};
26

27. Core API
class DeepNeuralNetwork {
public:
/**
* @brief setCallback
* @param callback. Callback function for the prediction result.
*/
virtual void setCallback(std::function<void(const unsigned long &, const kpsr::ai::F32AlignedVector &)> callback) = 0;
/**
* @brief predict. Load input matrix as input to network.
* @param inputVector. An F32AlignedVector of floats containing network input.
*
* @return Unique id corresponding to the input vector
*/
virtual unsigned long predict(const kpsr::ai::F32AlignedVector& inputVector) = 0;
/**
* @brief predict. Copy-less version of predict.
* @param inputVector. An F32AlignedVector of floats containing network input.
*
* @return Unique id corresponding to the input vector
*/
virtual unsigned long predict(const std::shared_ptr<kpsr::ai::F32AlignedVector> & inputVector) = 0;
};
27

28. Part 3
KATESU Project

29. QORIQ® LAYERSCAPE LS1046A
MULTICORE PROCESSOR
QorIQ® Layerscape LS1046A
Klepsydra AI Container

30. STATUS
• Successful installation of the following setup:
• LS1046 running Yocto Jethro
• Docker Installed on LS1046
• Container with the following:
• Ubuntu 20.04
• Klepsydra AI software fully supported (quantised and non-
quantised)

31. XILINX ZEDBOARD
ZedBoard
Klepsydra AI Container
PetaLinux
Klepsydra AI Container

32. STATUS
• Successful installation of the following setup:
• ZedBoard running PetaLinux 2019.2
• Docker Installed on ZedBoard
• Container with the following:
• Ubuntu 20.04
• Klepsydra AI software with quantised support only

33. Part 3.2
Performance
Results

34. PERFORMANCE RESULTS: CME ON LS1046
0
6,5
13
19,5
26
CPU / Hz
TFLite + NEON Klepsydra
0
45
90
135
180
Latency (ms)
TFLite + NEON Klepsydra
0
4,5
9
13,5
18
Throughput (Hz)
TFLite + NEON Klepsydra

35. PERFORMANCE RESULTS: CME-Q ON LS1046
0
6,75
13,5
20,25
27
CPU / Hz
TFLite + NEON Klepsydra
0
30
60
90
120
Latency (ms)
TFLite + NEON Klepsydra
0
7,5
15
22,5
30
Throughput (Hz)
TFLite + NEON Klepsydra

36. PERFORMANCE RESULTS: CME-Q ON ZEDBOARD
0
12,5
25
37,5
50
CPU / Hz
TFLite + NEON Klepsydra
0
250
500
750
1000
Latency (ms)
TFLite + NEON Klepsydra
0
0,65
1,3
1,95
2,6
Throughput (Hz)
TFLite + NEON Klepsydra

37. PERFORMANCE RESULTS: BSC ON LS1046
0
20
40
60
80
CPU / Hz
TFLite + NEON Klepsydra
0
1250
2500
3750
5000
Latency (ms)
TFLite + NEON Klepsydra
0
0,15
0,3
0,45
0,6
Throughput (Hz)
TFLite + NEON Klepsydra

38. Part 3.3
BSC Demo

39. Part 4
Conclusions
and Future
work

40. Vision-based navigation Earth Observation Telecommunications
• Process more images per
second
• Increase con
fi
dence in the
mission
• Reduce power consumption up
to 50%
• Faster access to data from Earth
• Increase processed request per
second (increase revenue)
• Enable AI telecomm (Cognitive
radios)
APPLICATION TO SPACE
40

41. ROADMAP
• 2022:
• Klepsydra AI for GPU (Q4 2022)
• 2023:
• Klepsydra AI For FGPA (Q2 2023)
• Support for operating systems: FreeRTOS (Q3 2023)
• Support for processor architectures:
• GR740 and GR765 (Q4 2023)
• RISC-V (Q4 2023)
• HPDP (Q4 2023)

42. CONTACT INFORMATION
Dr Pablo Ghiglino
pablo.ghiglino@klepsydra.com
+41786931544
www.klepsydra.com
linkedin.com/company/klepsydra-technologies