numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications. In this presentation we explain the work organization for what concerns the Xilinx Open Hardware Contest 2017.

1. numPYNQ
Work Organization
Riccardo Pressiani riccardo.pressiani@mail.polimi.it
Luca Stornaiuolo luca.stornaiuolo@mail.polimi.it
Filippo Carloni filippo.carloni@mail.polimi.it

2. numPYNQ
Gantt Diagram
2
March 1 June 30

3. numPYNQ
Gantt Diagram
3
March 1 June 30

4. numPYNQ
Gantt Diagram
4
March 1 June 30

5. numPYNQ
Gantt Diagram
5
March 1 June 30

6. numPYNQ
SWOT Analysis
6

7. numPYNQ
Strengths
7
• Provide to users the possibility to exploit the FPGA
technology directly from Python
• Increase users applications performance
• Provide less power consumption than competitors
acceleration (CUDA)

8. numPYNQ
Weaknesses
8
• PYNQ is a new product, so it is not yet mature
• Designing applications for PYNQ requires a lot of time and
specific knowledge
• PYNQ resources are limited with respect to similar devices

9. numPYNQ
Opportunities
9
• Our implementation can expand the PYNQ features and can
add interest in the product
• Within data science fields there is the need of systems that
can process data, at higher and higher speeds
• Many researchers are interested in accelerate numPY

10. numPYNQ
Threats
10
• There are several competitors, such as NVIDIA (CUDA) and Intel
(Altera FPGA and CPU on same chip)
• The companies are investing a lot of money in numPY
acceleration

11. numPYNQ 11
Luca Stornaiuolo
luca.stornaiuolo@mail.polimi.it
Riccardo Pressiani
riccardo.pressiani@mail.polimi.it
Filippo Carloni
filippo.carloni@mail.polimi.it
Thanks for the attention!

12. numPYNQ
an efficient implementation
of the NumPy library for PYNQ
https://twitter.com/numPYNQ_NECST
https://www.facebook.com/numPYNQ
https://www.slideshare.net/numPYNQ_at_NECST