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6. Brain NECSTwork: Implementation

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This is our overview on the Brain NECSTwork's implementation.
Discoveries associated with a more precise comprehension of the connections inside human brain are foreseen as disruptive in many fields: from improved neurological disorders treatment to strong artificial intelligence, as well as more precise and less invasive diagnostic tools and, finally, improved Big Data systems. For this purpose, Brain Networks (BNs) are used to quickly and accurately model and map neural interconnections inside human brain.
A common statistical tool that helps analysis and definition of BNs is the Pearson's Correlation Coefficient (PCC), which is able to identify the correlation between neurons or groups of neighboring neurons.
However, the computational power that commonly available technologies provide allows scientists to analyze only few hundred neural nodes within reasonable time. Increasing the number of analyzed neurons and speeding up the computation are both fundamental steps to achieve more accurate results, and to allow the scientific and medical research to progress.
This work presents an implementation of BNs on Xilinx VIRTEX-7 FPGA. Our goal is to tackle the problems previously described, in order to provide a fast hardware implementation able to support the computation of a remarkable number of neurons.

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6. Brain NECSTwork: Implementation

  1. 1. Brain NECSTworkEleonora D'Arnese eleonora.darnese@mail.polimi.it Enrico Reggiani enrico2.reggiani@mail.polimi.it Marco Gucciardi marco.gucciardi@mail.polimi.it image from http://i1-news.softpedia-static.com/images/news2/The-Brain-Super-Sized-Computer-Going-from-Internet-to-Fiber-Optics-2.jpg
  2. 2. Images are taken from fMRI Linearization of the images Pearson’s Correlation Coefficient Images with specific colored areas Reconstruction of images Brain NECSTwork 2
  3. 3. 3 Images Acquisition O2 More active areas of the brain receive more oxygenated blood Blood Oxygen-Level Dependent (BOLD) signal Images taken from functional Magnetic Resonance Imaging
  4. 4. Images are taken from fMRI Linearization of the images Pearson’s Correlation Coefficient Images with specific colored areas Reconstruction of images Brain NECSTwork 4
  5. 5. Image Linearization We used Matlab to acquire the images:  Fast (14 sec on GPU/200 img)  Extracts the matrix from the images easily 5
  6. 6. Image Linearization We used Matlab to acquire the images:  Fast (14 sec on GPU/200 img)  Extracts the matrix from the images easily …Images are now ready to be analysed! 5
  7. 7. Images are taken from fMRI Linearization of the images Pearson’s Correlation Coefficient Images with specific colored areas Reconstruction of images Brain NECSTwork 6
  8. 8. 7 Hardware Implementation Hardware Design is created and FPGA can be programmed PCC IP core is synthetized Pearson’s Correlation Coefficient (PCC) C code implementation
  9. 9. 7 Hardware Implementation Pearson’s Correlation Coefficient (PCC) C code implementation 𝑟 = 𝑖=1 𝑛 (𝑥𝑖 − 𝑥)(𝑦𝑖 − 𝑦) 𝑖=1 𝑛 𝑥𝑖 − 𝑥 2 𝑖=1 𝑛 𝑦𝑖 − 𝑦 2 r is the index which represents the value of the Pearson’s Correlation: with x, y selected pixels and 𝑥, 𝑦 mean values
  10. 10. Hardware Implementation PCC IP core is synthetized Pearson’s Correlation Coefficient (PCC) C code implementation  Obtainment of a high level code from the PCC C implementation by Vivado HLS  PCC IP core can be used for the hardware implementation 7
  11. 11. Hardware Implementation Hardware Design is created and FPGA can be programmed PCC IP core is synthetized Pearson’s Correlation Coefficient (PCC) C code implementation 7
  12. 12. Images are taken from fMRI Linearization of the images Pearson’s Correlation Coefficient Images with specific colored areas Reconstruction of images Brain NECSTwork 8
  13. 13. Colored areas  Correlation shown by colored areas of the brain  C code will consider the average response to a stimulus  Activated areas will be highlighted depending on a time threshold  C code  OpenCL on GPU 9
  14. 14. Colored areas  Correlation shown by colored areas of the brain  C code will consider the average response to a stimulus  Activated areas will be highlighted depending on a time threshold  C code  OpenCL on GPU …But we are still working on this! www.familyhappening.it 9
  15. 15. Images are taken from fMRI Linearization of the images Pearson’s Correlation Coefficient Images with specific colored areas Reconstruction of images Brain NECSTwork 10
  16. 16. Eleonora D’Arnese – eleonora.darnese@mail.polimi.it Enrico Reggiani – enrico2.reggiani@mail.polimi.it Marco Gucciardi – marco.gucciardi@mail.polimi.it https://m.facebook.com/BrainNECSTwork https://twitter.com/Brain_NECSTwork?s=08 http://www.slideshare.net/BrainNECSTwork Contacts 16

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