OSGi Community Event 2016 Presentation by Tim Verbelen (iMinds / imec) With the current explosion of IoT devices connected to the Internet, the biggest challenge in the near future is how to process and analyze all this generated data, making use of the highly distributed compute infrastructure at hand. A promising approach for data analysis is deep learning, using brain-inspired neural networks for feature extraction and detection. In our research lab, we have developed DIANNE, an OSGi-based framework for creating, deploying and training artificial neural networks in a modular way. Benefiting from OSGi modularity, we can easily distribute (parts of) the neural networks among cloud and edge devices.

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Tim Verbelen – imec, Ghent Unversity
DIANNE, A DISTRIBUTED DEEP LEARNING FRAMEWORK ON OSGI

2. SMART?
THE FALLACIES OF THE SMART EVERYTHING...
CONNECTED!

3. A TRULY SMART SYSTEM IS ABLE TO LEARN
Image captioning (Vinyals et al)
Speech synthesis (van den Oord et al)
Speech recognition systems
AlphaGo
(Silver et al.)
Robot tasks (Abbeel et al)

4. MACHINE LEARNING WITH DEEP NEURAL NETWORKS
4
Neural networks are trained to approximate any function
Difficulties:
Requires a lot of computation
Requires a huge amount of data
GPU / CLOUD COMPUTING
INTERNET OF THINGS

5. WHERE TO DEPLOY?
5
DISTRIBUTED INFRASTRUCTURE REQUIRES DISTRIBUTED SOLUTION
IoT
gateway
Cloud
Sensor/Edge nodes
End-user devices

6. IMAGE DIVIDER. POSITION
THE TITLE ACCORDING TO
THE IMAGE. USE THE WHITE
TITLE FOR DARK IMAGES.
KEY PRINCIPLES

7. MODULARITY
7
DECOUPLE INTERFACE FROM IMPLEMENTATION USING SERVICES

8. MODULARITY IN DIANNE (1)
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MODULES ARE BASIC BUILDING BLOCKS FOR DEFINING NEURAL NETWORK

9. MODULARITY IN DIANNE (2)
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MODULAR BUILDING BLOCKS FOR LEARNING AND EVALUATING NEURAL
NETWORKS
Neural
Network
Repository
Dataset
Learner Evaluator Agent

10. MODULARITY IN DIANNE (3)
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USE THE MODULES AS LEGO BLOCKS TO BUILD YOUR NEURAL NETWORK

11. IMAGE DIVIDER. POSITION
THE TITLE ACCORDING TO
THE IMAGE. USE THE WHITE
TITLE FOR DARK IMAGES.
DEMO TOUR

12. EXAMPLE CHARTS
WHY OSGI?

13. REMOTE SERVICES
13
A B
RSA RSA
exportServiceimportService
CALLING OSGI SERVICES ON DISTRIBUTED INFRASTRUCTURE

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public class
NeuralNetworkInstanceDTO {
public UUID id;
public String description;
public String name;
public Map<UUID,
ModuleInstanceDTO>
modules;
}
public interface NeuralNetwork {
UUID getId();
NeuralNetworkInstanceDTO
getNeuralNetworkInstance();
Promise<NeuralNetworkResult>
forward(...);
Promise<NeuralNetworkResult>
backward(...);
...
}
DATA
BEHAVIOR
DTOSERVICE
DTOS
SPLITTING DATA FROM BEHAVIOR

15. DTOS (2)
15
GREAT FOR CONFIGURATION
public class SGDConfig {
public float learningRate = 0.01f;
public float minLearningRate = 0.0f;
public float decayRate = 0.0f;
…
}
Map<String, String> config …
SGDConfig c = DianneConfigHandler
.getConfig(config, SGDConfig.class)
SGDProcessor p
= new SGDProcessor(c);
How to provide configuration to objects *
Constructor/method parameters?
DTO?
Map<String, String>?
* for configuring services, use ConfigAdmin

16. NATIVE LIBRARIES
16
FRAGMENTS PROVIDE THE RIGHT NATIVE LIB FOR YOUR PLATFORM
Tensors are residing only in native (GPU) memory
Build a fragment with Bundle-NativeCode header for each supported
platform
At runtime, the right fragment resolves and is attached to the host
bundle providing the Java objects with native methods
Caveat: correct fragment for GPU still has to be selected manually (not
automatically provided as capability)

17. OVERHEAD?
17

18. PROMISES
18
DIANNE Neural network modules are executed asynchronously
NeuralNetwork provides API with promises
final NeuralNetwork nn = ...;
Tensor batch = loadBatch();
Tensor nextBatch;
while(learning){
// do forward-backward pass during training
Promise result = nn.forward(batch,…).then( p → {
Tensor output = p.getValue().tensor;
Tensor grad = calculateGradient(output);
return nn.backward(grad);
}).then(…);
nextBatch = loadBatch(); // already load next batch
result.getValue(); // wait for async completion
… // flip nextBatch and batch
}

19. CONCIERGE
19
A small footprint OSGi core implementation
All demos shown during this presentation were running on Concierge
Learn more tomorrow: “Getting to the Next Level with Eclipse
Concierge” - Schubartsaal, 17h45

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http://dianne.intec.ugent.be/
git clone https://github.com/ibcn-cloudlet/dianne.git
THANK YOU!

22. PUBLIC