NoSDSE

Network-of-Systems Design Space Exploration

NoSDSE [2] is a network-level design space exploration framework resource-constrained Networks-of-Systems (NoS). It relies on NoSSim [1], a fast and accurate NoS simulation framework, for design point evaluation. NoSDSE further includes NoSSim model generators and a multi-objective genetic algorithm to allow automated NoS design space exploration.

NoSDSE is demonstrated with three IoT/mobile application scenarios.

IoT scenarios:

  examples/vision_graph       -- Vision graph discovery, where the relative position and 
		                 orientation among a network of smart cameras are estimated
		      
  examples/ecg_diagnosis      -- ECG monitoring, where raw ECG signals are used to 
		                 detect heart arrhythmia

  examples/deepthings         -- DeepThings is a framework for locally distributed and adaptive CNN 
                                 inference in resource-constrained IoT edge clusters [3]

Directories:

  tools/                     -- JSON lib and configuration file template
  NoSSim/                    -- NoSSim, a network/system co-simulation framework
  examples/vision_graph/     -- The vision graph discovery example
     ezSIFT/                     - The SIFT (scale-invariant feature transform) algorithm library
     networking_api/             - Networking APIs and runtime libraries (based on lwip and HCSim) 
     src/                        - Source code for application, system and network models
     dse.py                      - Model generators (From genome to NoSSim simulation instance)
     nsga.py                     - Design space explorer based on NSGA-II

  examples/ecg_diagnosis/    -- The ECG monitoring example
     ecg/     	                 - ECG diagnosis library
     networking_api/             - Networking APIs and runtime libraries (based on lwip and HCSim) 
     src/                        - Source code for application, system and network models
     dse.py                      - Model generators (From genome to NoSSim simulation instance)
     nsga.py                     - Design space explorer based on NSGA-II

  examples/deepthings/    -- The DeepThings example
     application/     	         - DeepThings source code and Darknet library
     networking_api/             - Distributed work stealing runtime (based on lwip and HCSim) 
     src/                        - Source code for application, system and network models
     dse.py                      - Model generators (From genome to NoSSim simulation instance)
     nsga.py                     - Design space explorer based on NSGA-II

Building and installing:

Build requirements:

• SystemC version 2.3.1 http://www.accellera.org/
• OMNEST version 5.0 http://www.omnest.com/ (alternatively OMNeT++ version 5.0 with SystemC integration http://www.omnetpp.org)
• INET framework version 3.4.0 http://inet.omnetpp.org/

Preparation before build:

• For setting up HCSim (included as a submodule), please see here
• Apply the INET patch (inet_extra) and rebuild INET framework

Preparation before running the example:

• Set the OMNeT++ path:

  pushd /home/slam/OMNET/omnetpp-5.0; . setenv; popd

• Set the INET path by changing the INET_DIR in Makefile for each application

Running:

In general, you should perform a function-level profiling and back-annotation for the lwIP, ECG and ezSIFT library with the provided LLVM pass and helper functions in InstrumentLLVM directory. For the case studies included in this repository, we include profile data for different platforms so that you can directly try them out of box.

For DeepThings, you need to download the weight file for YOLOv2 and put it in examples/deepthings/src/models.

To build an example, change into the corresponding example directory and run:

   cd examples/vision_graph
   make dependency
   make makefiles
   make

To perform exploration for an example:

   cd examples/ecg_diagnosis
   python nsga.py

References:

• [1] Z. Zhao, V. Tsoutsouras, D. Soudris and A. Gerstlauer, "Network/System Co-Simulation for Design Space Exploration of IoT Applications," SAMOS, July 2017.
• [2] Z. Zhao, K. Mirzazad, A. Gerstlauer, "Network-level Design Space Exploration for Resource-Constrained Networks-of-Systems," ACM Transactions on Embedded Computer Systems (TECS), under review.
• [3] Z. Zhao, K. Mirzazad, A. Gerstlauer, "DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters," CODES+ISSS, special issue of IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2018

Contact:

 Zhuoran Zhao <[email protected]>
 Kamyar Mirzazad <[email protected]>