Hardware Acceleration for Data Processing (HADP) - Fall 2017

NEWS: 

    Don't forget to submit your reports! Deadline is 22.12.2017. The reports and any comments should be sent to tal.bennun {at) inf ethz ch

     

     

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Overview

The seminar is intended to cover recent results in the increasingly important field of hardware acceleration for data science, both in dedicated machines or in data centers. The seminar aims at students interested in the system aspects of data processing who are willing to bridge the gap across traditional disciplines: machine learning, databases, systems, and computer architecture. The seminar should be of special interest to students interested in completing a master thesis or even a doctoral dissertation in related topics.

Format

The seminar will start on September 19th with an overview of the general topics and the intended format of the seminar. Students are expected to present one paper in a 30 minute talk and complete a 4 page report on the main idea of the paper and how they relate to the other papers presented at the seminar and the discussions around those papers. The presentation will be given during the semester in the allocated time slot. The report is due on the last day of the semester.

Attendance to the seminar is mandatory to complete the credit requirements. Active participation is also expected, including having read every paper to be presented in advance and contributing to the questions and discussions of each paper during the seminar.

 


Course Material


 Schedule

NAME PAPER DATE MENTOR
 Nils Weller  PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning  Oct 10  Onur Mutlu
 Luca Gasparini Hybrid Row-Column Partitioning in Teradata  Oct 17  Ce Zhang
 Conradin Roffler Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds  Oct 17  Muhsen Owaida
 Andreas Zingg Genomic Data Clustering on FPGAs for Compression  Oct 24  Ce Zhang
 Philipp Schaad FASTEN: An FPGA-based Secure System for Big Data Processing  Oct 24  Muhsen Owaida
 Natallie Baikevich XGBoost: A Scalable Tree Boosting System  Oct 31  Ce Zhang
 Benjamin Rothenberger Live Video Analytics at Scale with Approximation and Delay-Tolerance  Oct 31  Gustavo Alonso
 Hanjing Gao  A Scalable Processing-in-Memory Accelerator for Parallel Graph Processing  Nov 7  Onur Mutlu
 Andrawes Al Bahou The microarchitecture of a real-time robot motion planning accelerator  Nov 14  Onur Mutlu
 Lavrenti Frobeen Asynchronous Methods for Deep Reinforcement Learning  Nov 14  Tal Ben-Nun
 Geethan Karunaratne BlueDBM: An Appliance for Big Data Analytics  Nov 21  Gustavo Alonso
 Ali Smesseim In-Datacenter Performance Analysis of a Tensor Processing Unit  Nov 21  Torsten Hoefler
 Zhenhao He Energy-Efficient Acceleration of Big Data Analytics Applications Using FPGAs  Nov 28  Muhsen Owaida
 Jiawei Liao DianNao: A Small-Footprint High-Throughput Accelerator for Ubiquitous Machine-Learning  Nov 28  Tal Ben-Nun
 Andreas Kurth Can FPGAs beat GPUs in Accelerating Next-Generation Deep Neural Networks?  Dec 5  Gustavo Alonso

 

Seminar Hours

Tuesdays, 13:00-15:00 in ML J 34.1

Lecturers: