DMCA
An Automated Framework for Accelerating Numerical Algorithms on Reconfigurable Platforms Using Algorithmic/Architectural Optimization,” in (2009)
| Venue: | IEEE Transactions on Computers |
| Citations: | 4 - 1 self |
BibTeX
@ARTICLE{Kim09anautomated,
author = {Jung Sub Kim and Lanping Deng and Prasanth Mangalagiri and Kevin Irick and Kanwaldeep Sobti and Mahmut K and Vijaykrishnan Narayanan and Chaitali Chakrabarti and Nikos Pitsianis and Xiaobai Sun},
title = {An Automated Framework for Accelerating Numerical Algorithms on Reconfigurable Platforms Using Algorithmic/Architectural Optimization,” in},
journal = {IEEE Transactions on Computers},
year = {2009},
pages = {1654--1667}
}
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Abstract
Abstract—This paper describes TANOR, an automated framework for designing hardware accelerators for numerical computation on reconfigurable platforms. Applications utilizing numerical algorithms on large-size data sets require high-throughput computation platforms. The focus is on N-body interaction problems which have a wide range of applications spanning from astrophysics to molecular dynamics. The TANOR design flow starts with a MATLAB description of a particular interaction function, its parameters, and certain architectural constraints specified through a graphical user interface. Subsequently, TANOR automatically generates a configuration bitstream for a target FPGA along with associated drivers and control software necessary to direct the application from a host PC. Architectural exploration is facilitated through support for fully custom fixed-point and floating point representations in addition to standard number representations such as single precision floating point. Moreover, TANOR enables joint exploration of algorithmic and architectural variations in realizing efficient hardware accelerators. TANOR’s capabilities have been demonstrated for three different N-body interaction applications: the calculation of gravitational potential in astrophysics, the diffusion or convolution with Gaussian kernel common in image processing applications, and the force calculation with vector-valued kernel function in molecular dynamics simulation. Experimental results show that TANOR-generated hardware accelerators achieve lower resource utilization without compromising numerical accuracy, in comparison to other existing custom accelerators. Index Terms—Algorithms implemented in hardware, Reconfigurable hardware, Signal processing systems, Numerical algorithms. F 1
Keyphrases
automated framework reconfigurable platform algorithmic architectural optimization accelerating numerical algorithm single precision joint exploration numerical algorithm tanor design flow tanor-generated hardware accelerator efficient hardware accelerator n-body interaction problem associated driver wide range custom fixed-point high-throughput computation platform host pc matlab description certain architectural constraint configuration bitstream graphical user interface control software target fpga different n-body interaction application signal processing system architectural exploration force calculation architectural variation numerical computation vector-valued kernel function index term algorithm image processing application reconfigurable hardware tanor capability application utilizing numerical algorithm molecular dynamic simulation hardware accelerator number representation custom accelerator gaussian kernel large-size data set numerical accuracy gravitational potential particular interaction function point representation experimental result resource utilization
