This is a brief presentation of our 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.

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. 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
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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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. 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. 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
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