Paper Info
Title
Fast and efficient power estimation model for FPGA based designs.
Abstract
With the growing scope of embedded computing, specific design objectives demand quick exploration and estimation of design metrics. Power estimation is one such primary design metric which needs to be estimated at the earliest stage of high-level design. In this paper, a model is presented to estimate dynamic power requirement of any given application for a target field-programmable gate array (FPGA) device. The methodology comprises of the profiling of the C/C++ programs through a low-level virtual machine (LLVM) pass and training of an artificial neural network model using the profiling results to estimate power. For profiling an application, the LLVM based framework is employed, which generates target independent LLVM intermediate representation (IR). A module pass is written to obtain the count of the different type of instructions. A model using artificial neural network has been proposed to give the power estimate, which takes as inputs the category-wise number of instructions and FPGA target resources on which the respective applications are mapped. The Zynq family device is profiled using Vivado HLS v.2015.4. The model has been validated against CHStone benchmark programs. Furthermore, a reduced relative error of 0.19% to 7.9% is observed for the analyzed benchmark designs, with the exceptional increase in estimation speed, which is more than the order of magnitude of the conventional Xilinx Vivado Design Suite. Therefore, for designers, this modeling methodology provides better, accurate and fast power estimation, at the early stage of the VLSI design.
Year
DOI
Venue
2018
10.1016/j.micpro.2018.03.005
Microprocessors and Microsystems
Keywords
Field
DocType
Design space exploration,High-level synthesis,Power estimation,Field programmable gate array (FPGA),Low level virtual machine intermediate representation (LLVM IR),Artificial neural network (ANN)
Virtual machine,Profiling (computer programming),Computer science,Field-programmable gate array,Real-time computing,Gate array,Dynamic demand,Artificial neural network,Very-large-scale integration,Computer engineering,Design objective
Journal
Volume
ISSN
Citations 
59
0141-9331
0
PageRank 
References 
Authors
0.34
19
2
Name
Order
Citations
PageRank
Abhishek Tripathi1606.35
Arvind Rajawat2123.84