Quantization Error and Accuracy-Performance Tradeoffs for Embedded Data Mining Workloads

Authors:
Ramanathan Narayanan;Berkin Özışkyılmaz;Gokhan Memik;Alok Choudhary;Joseph Zambreno
Affiliations:
Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

Citing 5
Cited 2

Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware

FPGA '01 Proceedings of the 2001 ACM/SIGDA ninth international symposium on Field programmable gate arrays
Automatic floating-point to fixed-point conversion for DSP code generation

CASES '02 Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems
NeuroFPGA -- Implementing Artificial Neural Networks on Programmable Logic Devices

Proceedings of the conference on Design, automation and test in Europe - Volume 3
An Algorithm for Trading Off Quantization Error with Hardware Resources for MATLAB-Based FPGA Design

IEEE Transactions on Computers
Efficient Hardware Data Mining with the Apriori Algorithm on FPGAs

FCCM '05 Proceedings of the 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines

Dynamic knobs for responsive power-aware computing

Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
Byte-precision level of detail processing for variable precision analytics

SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data mining is the process of automatically finding implicit, previously unknown and potentially useful information from large volumes of data. Embedded systems are increasingly used for sophisticated data mining algorithms to make intelligent decisions while storing and analyzing data. Since data mining applications are designed and implemented considering the resources available on a conventional computing platform, their performance degrades when executed on an embedded system. In this paper, we analyze the bottlenecks faced in implementing these algorithms in an embedded environment and explore their portability to the embedded systems domain. Particularly, we analyze the floating point computation in these applications and convert them into fixed point operations. Our results reveal that the execution time of five representative applications can be reduced by as much as 11.5脳 and 5.2脳 on average, without a significant impact on accuracy.