This presentation focuses on our project plan. 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 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. 2
Work Plan
Research field
definition
Correlation analysis
from fMRI images
Analysis of SoA
Problem identification
Pearson’s Correlation
Coefficient (PCC)
Step 1
Milestone
Task

3. 3
Work Plan
Acceleration of PCC
on FPGA
Test and validation
Step 1 Step 2
PCC SW
implementation
PCC HW
implementation
Test and validation
Milestone
Task

4. 4
Work Plan
Step 3Step 1 Step 2
Milestone
Task
FPGA correlation
results
Graphical SW
implementation
Final graphical
representation
Test and validation

5. Gantt Chart
5

6. 6
SWOT Analysis
Lower energy consumption
Skills in both biomedical and HW
implementation fields
Clearer results that reduce the
error’s chance
Decrease of execution time

7. 6
SWOT Analysis
Lower energy consumption
Skills in both biomedical and HW
implementation fields
Clearer results that reduce the
error’s chance
Decrease of execution time
No skills in 3D rendering
Compatible only with
certain type of images
(fMRI)
Inability to extract brain's images

8. 6
SWOT Analysis
Lower energy consumption
Skills in both biomedical and HW
implementation fields
Clearer results that reduce the
error’s chance
Decrease of execution time
No skills in 3D rendering
Compatible only with
certain type of images
(fMRI)
Inability to extract brain's images
Possible improvement of
technologies in BN field
Improvement and speed
up of the diagnosis
process

9. 6
SWOT Analysis
Lower energy consumption
Skills in both biomedical and HW
implementation fields
Clearer results that reduce the
error’s chance
Decrease of execution time
No skills in 3D rendering
Compatible only with
certain type of images
(fMRI)
Inability to extract brain's images
Possible improvement of
technologies in BN field
Improvement and speed
up of the diagnosis
process
Other method used to analyse
Brain Networks could reach the
same results in a more efficient
way

10. 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
10