The document describes numPYNQ, a hardware library that offers an accelerated version of NumPy for use on FPGAs. It aims to provide high performance and low energy consumption for tasks like signal processing, machine learning, and image analysis. The library addresses challenges of FPGA development like performance, energy usage, and design time. It uses an overlay and Python API to make FPGA programming accessible for data scientists. The presentation outlines numPYNQ's technical approach, potential clients in big data companies, go-to-market strategy, revenue model, competition, and roadmap.

1. StartUp 101
numPYNQ
21/06/17, DEIB Seminar Room
Luca Stornaiuolo
Riccardo Pressiani
Filippo Carloni
luca.stornaiuolo@mail.polimi.it
riccardo.pressiani@mail.polimi.it
filippo.carloni@mail.polimi.it

2. CONTEXT DEFINITION
2
Signal Processing Machine LearningData MiningImage Analysis
Huge amount of data that need to be processed to get aggregated information

3. TECHNICAL CHALLENGES
3
Performance Energy Consumption Design Time
The need of higher and higher computational power brings
some challenges to overcome

4. SOLUTION
4
numPYNQ
numPYNQ is an FPGA-based hardware library that offers all
the data scientists and software developers an accelerated
version of the core functions of NumPy to be used
transparently from their applications.

5. INNOVATION & TECHNOLOGY
5
Performance
Energy Consumption

6. INNOVATION & TECHNOLOGY
6
Performance
Energy Consumption
Design Time

7. INNOVATION & TECHNOLOGY
7
Overlay
Bitstream Python API

8. MARKET
8
The Big Data market is estimated to top $61 billion in 2020, a
26% compound annual growth rate for 2011-2020.
Source: Wikibon research as covered by KDNuggets.com

9. CLIENTS
9
Two feasible options
Data Analysis-based
Companies

10. CLIENTS
10
• The PYNQ platform goal is to let FPGA non-expert users
to easily exploit the power of such devices
• numPYNQ would expand the available features for the
PYNQ platform
• Xilinx would be able to increase the revenues for the
PYNQ-based products

11. CLIENTS
11
Data Analysis-based
Companies
• Data scientists need high-performing, flexible and easy
to use systems
• FPGAs are a great opportunity, although the high
expertise and the design time required are often an
important barrier
• Data scientists could save money and time exploiting the
numPYNQ features

12. GO TO MARKET
12
Data Analysis-based
Companies
Partnership deal to complete the
development with the company support
Revenue sharing
Direct channel marketing through
sales force, email and online marketing
numPYNQ as a Service

13. REVENUE MODEL
13
Freemium model
Basic Plan Premium Plan Advanced Plan
Basic functions
Advanced Functions —
Dedicated Training —
Dedicated Support —
Custom Functions — —
Cost Free $ $$$

14. REVENUE MODEL
14
An online store to sell packages of advanced functions
to extend the Freemium Plans

15. COMPETITION ANALISYS
15
numPYNQ
TensorFlow
PyCUDA

16. ROADMAP
16
Jun
‘17
M
ar‘18
Jun
‘18
D
ec‘18
Proof Of Concept
at XOHW17*
Second Phase
of S2P Contest
Complete development
of the library
Porting on
F1 Instances
Business Model
Testing
* Xilinx Open Hardware Contest 2017: http://www.openhw.eu

17. TEAM
17
Luca Stornaiuolo
MSc student in Computer Science and Engineering at Politecnico di Milano
Role in numPYNQ project: Hardware Design and Optimization Manager
Riccardo Pressiani
MSc student in in Computer Science and Engineering at Politecnico di Milano and
University of Illinois at Chicago
Role in numPYNQ project: Hardware Implementation and Social Media Manager
Filippo Carloni
BSc student in Computer Science and Engineering at Politecnico di Milano
Role in numPYNQ project: PYNQ Overlay and Interfaces Manager

18. CONTACTS
18
numPYNQ
https://www.facebook.com/numPYNQ
https://www.slideshare.net/numPYNQ_at_NECST
https://twitter.com/numPYNQ_NECST
Thanks for the attention!
Luca Stornaiuolo
Riccardo Pressiani
Filippo Carloni
luca.stornaiuolo@mail.polimi.it
riccardo.pressiani@mail.polimi.it
filippo.carloni@mail.polimi.it