PUMA Simulator

Below you will find some information on how to use the PUMA Simulator.

Table of Contents

• Introduction
• System requirements
• Quick Start
• Usage
• Citation
• Authors

Introduction

This emulator was created as part of the PUMA work presented at ASPLOS 2019.

Paper: https://dl.acm.org/citation.cfm?id=3304049 (see details on architecture and compiler here).

Slides: https://www.dropbox.com/s/mszdkbhwzv0yv4q/slides-puma-asplos19.pptx?dl=0

Lightning Talk:https://www.youtube.com/watch?v=x3dAPgkQFOg

System requirements

Below you can find the system requirements and versions tested.

Requirement	Version
OS: Ubuntu	16.04.3 LTS (Xenial Xerus)
Python	2.7.12

Quick Start

sudo apt-get install python-tk

sudo pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp27-cp27mu-linux_x86_64.whl

sudo pip install -r <dpe_emulate>/requirements.txt

If you are behind a proxy, you should type sudo pip --proxy $http_proxy install ... instead.

Usage

Emulate

For testing if everything is working fine:

cd <puma-simulator>/src

python dpe.py

Then, you should see some results like:

...

('Cycle: ', 8783, 'Tile halt list', [1, 1, 0, 1])
('Cycle: ', 8784, 'Tile halt list', [1, 1, 0, 1])
('Cycle: ', 8785, 'Tile halt list', [1, 1, 0, 1])
('Cycle: ', 8786, 'Tile halt list', [1, 1, 1, 1])
cycle: 8786 Node Halted
Finally node halted | PS: max_cycles 10000
('Dumping tile num: ', 0)
('Dumping tile num: ', 1)
('Dumping tile num: ', 2)
('Dumping tile num: ', 3)
Output Tile dump finished
Success: Hadrware results compiled!!

Running a compiled model

This emulator executes models compiled by PUMA Compiler (https://github.com/illinois-impact/puma-compiler).

After you compile a model using the compiler, in order to execute it with the DPE emulator, follow the steps below:

1- Copy /test/generate-py.sh and /test/input.py files to the /test folder (you only need to do this once).

2- Edit /test/generate-py.sh file, line 4 and change the value of SIMULATOR_PATH variable.

SIMULATOR_PATH="<emulator_root_path>"

3- Execute the generate-py.sh script

4- Look at the number of tiles generated by the compiler for ths model and update the value of "num_tile_compute" entry in /include/config.py file to match the number fo compute tiles generated by compiler (remember that Til0 and Tile1 are used for input and output)

5- Go to emulator src directory (/src) and execute dpe.py:

python2 dpe.py -n <model_name>

Citation

Please cite the following paper if you find this work useful:

• A. Ankit, I. El Hajj, S. Chalamalasetti, G. Ndu, M. Foltin, R. S. Williams, P. Faraboschi, W.-M. Hwu, J. P. Strachan, K. Roy, D. Milojicic. PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019.

Authors

Aayush Ankit, Plinio Silveira, Glaucimar Aguiar