Document Classification Systems in Heterogeneous Computing Environments

Nasiri, N., Colangelo, P., Segal, O., Margala, M. and Vanderbauwhede, W. (2017) Document Classification Systems in Heterogeneous Computing Environments. In: 26th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS 2016), Bremen, Germany, 21-23 Sept 2016, pp. 291-295. ISBN 9781509007332 (doi: 10.1109/PATMOS.2016.7833702)

Abstract

Datacenter workloads demand high throughput, low cost and power efficient solutions. In most data centers the operating costs dominates the infrastructure cost. The ever growing amounts of data and the critical need for higher throughput, more energy efficient document classification solutions motivated us to investigate alternatives to the traditional homogeneous CPU based implementations of document classification systems. Several heterogeneous systems were investigated in the past where CPUs were combined with GPUs and FPGAs as system accelerators. The increasing complexity of FPGAs made them an interesting device in the heterogeneous computing environments and on the other hand difficult to program using Hardware Description languages. We explore the trade-offs when using high level synthesis and low level synthesis when programming FPGAs. Using low level synthesis results in less hardware resource usage on FPGAs and also offers the higher throughput compared to using HLS tool. While using HLS tool different heterogeneous computing devices such as multicore CPU and GPU targeted. Through our implementation experience and empirical results for data centric applications, we conclude that we can achieve power efficient results for these set of applications by either using low level synthesis or high level synthesis for programming FPGAs.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Vanderbauwhede, Professor Wim
• Authors: Nasiri, N., Colangelo, P., Segal, O., Margala, M., and Vanderbauwhede, W.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781509007332
• Copyright Holders: Copyright © 2016 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher