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[[File:NeuroStream Arch.png|thumb|NeuroStream Architecture]]
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[[File:NeuroSoC Arch.png|thumb|NeuroCluster and NeuroSoC Architecture]]
 
  
 
==Short Description==
 
==Short Description==

Revision as of 20:00, 22 February 2018

ECG Task

Introduction

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Short Description

Your mission, should you choose to accept it, is to join our research into such near-memory computing either from the hardware or software side. The NeuroStream co-processor has been verified to perform the correct operations on a very small scale. However, to be a convincing solution to the problems it tries to tackle, and to further verify that it actually works and is up to the task, a larger scale implementation is needed. The IIS has access to a small number of interconnected FPGA computing nodes [4] through a joint research project with Microsoft. This thesis offers the opportunity to either adapt the existing NeuroStream/NeruoCluster HDL code to the FPGAs and develop a communication scheme among them, then squeeze as many NeuroClusters onto one FPGA as possible. Or you can dig into the software side, leveraging the compute capabilities of NeuroStream to implement the layers of common Deep Neural Networks (e.g. GoogLeNet [5]) or Recurrent Neural Networks [6,7]. The project is very flexible and we can tailor it to your personal preferences and skills!

Status: Available

Looking for interested master students (Semester or Master Project)
Supervision: Fabian Schuiki, Florian Zaruba

Character

20% Theory and Algorithms
50% Implementation (HDL or C/C++ coding)
30% Verification and Testing

Prerequisites

If the focus shall be on hardware:

VLSI I
VLSI II (recommended)
VHDL/SystemVerilog

If the focus shall be on software:

Knowledge in Machine Learning, or willingness to acquire such (DNN, LSTM/GRU)
C/C++

Professor

Luca Benini

References

[1] http://iis-projects.ee.ethz.ch/index.php/PULP
[2] http://www.hybridmemorycube.org/
[3] https://www.amd.com/Documents/High-Bandwidth-Memory-HBM.pdf
[4] https://www.microsoft.com/en-us/research/project/project-catapult/
[5] http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf
[6] http://colah.github.io/posts/2015-08-Understanding-LSTMs/
[7] http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/

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