The P.IZ.Z.A Team presented their project to accelerate the Umap algorithm through FPGA implementation. They introduced themselves and their goal to speed up the already fast Umap algorithm. Their project aims to accelerate the central core of the NN_Descent algorithm through an FPGA implementation plus optimized Python layer. Their current work includes a fully functioning FPGA implementation of the nn_descent algorithm using custom data types and optimized conditional jumps. They also have an automated build process to generate C code from the original Python implementation to run on the FPGA.

1. PROJECT PRESENTATION
DEIB Seminar Room - 05/06/2019
Giorgio Colomban giorgio.colomban@mail.polimi.it
Luca Carminati luca5.carminati@mail.polimi.it
Aaron Cettolin aaron.cettolin@mail.polimi.it

2. WHO ARE WE & WHAT DO WE DO?
Hello, we are the P.IZ.Z.A Team and we accelerate Umap
UMAP
Speeding up an already fast
algorithm

3. WORK CONTEXT?
Algorithm created and
available on github
Python algorithm
optimized by compilation
Numba

4. KPI
SPEED
POWER
CONSUMPTION

5. PROJECT IDEA?
Accelerate central core of NN_Descent + python layer
+

6. CURRENT STATE
FPGA IMPLEMENTATION
Fully working implementation of nn_descent that
runs on FPGA
Custom and optimized data types
Optimized conditional jumps: the program process
flow has been made more static by performing the same
kind of operations in if - else conditional statements

7. CURRENT STATE
PYTHON LAYER
Working automated build process, that runs the original python
implementation with a random input of the algorithm to get base
results, and that generates C code with the same random input data
as before, that can then go through Vivado to become runnable on
FPGA

8. THANKS FOR THE ATTENTION
QUESTIONS?
Contact us
Luca Carminati luca5.carminati@mail.polimi.it
Giorgio Colomban giorgio.colomban@mail.polimi.it
Aaron Cettolin aaron.cettolin@mail.polimi.it
pizzateampolimi@gmail.com @PIZZATEAM2PIZZA TEAM