Analysis Of Discrete Wavelet Transform For Optimum Machine Instruction Of Dlx Microprocessor

Abstract

Abstract The application of monitoring over Wireless Sensor Network (WSN) is highly demanded to be implemented in the Internet of Things (IoT). The problem that appears in IoT is the general purpose microprocessor is still highly used, which causes more energy used than it is needed. Although, an Application Specific Integrated Circuit (ASIC) can be used to make a more efficient energy application, it is more expensive and permanent, which means it can't be changed or reconfigured. This thesis presents a method to design a specific purpose microprocessor by compressing an image in DLX microprocessor, which can still be reconfigured by optimizing machine instruction needed in the microprocessor. Prior to DWT process, an image will go through pre-processing stage. The stage will be done in Matlab to turn an RGB image into a grayscale image, and the matrix of the grayscale image will be obtained. This matrix will be the input for Haar DWT machine instruction. The machine instruction is simulated in WinDLX, a simulator for DLX microprocessor. After the simulation has finished, the statistics of the simulation will be analyzed to conclude whether the machine instruction is optimum enough. The result of Haar DWT machine instruction is the same as the result obtained from Matlab, which means the machine instruction is capable to do the image compression. Out of 92 kinds of instruction, Haar machine instruction only needs 20 kinds of instructions used. This shows that the program will not waste energy for unused instruction. From the statistics obtained, the total cycles executed from the pipelined DLX microprocessor is 1239 cycles, where a non-pipelined microprocessor would need 2755 cycles to execute the program. This means the program is a more efficient method to run a Haar DWT compression. Keywords: optimum machine instruction, DLX microprocessor, DWT image compression, internet of things (IoT), wireless sensor multimedia networks. 1. Introduction As the technology grows more and more in human life, the phrase "Internet of Things" is not an uncommon phrase to be involved in the growth. Kevin Ashton was the first person to use the term “Internet of Things†in 1999. At that time, Kevin and his team were developing an extension of the internet to accommodate things and it inspired him to the term “Internet of Things†[1]. The idea of IoT was developed in parallel to WSNs [2]. Wireless Sensor Network or WSN is a network of a large number of nodes that cooperatively sense the environment. The application of the WSN has been done since the 1980s but then became more common to use in 2001 for industrial and research purposes [2]. The WSN is largely applied in many applications, such as environmental monitoring, industrial and infrastructure, and military surveillance. Although WSN is very useful for the convenience in the society, this technology also comes with some issues [3], such as the minimum exposure path [4] and the energy sink-hole [5], [6] in WSN. The main problem discusses in this thesis is the energy lifetime of the WSN itself, which people have been paying attention as well. Sensor nodes are usually powered by limited lifetime batteries. Changing the batteries frequently become very inefficient for a long use of WSN. There are many suggestions to this specific problem, such as wirelesspowered sensor networks [7] and harvesting solar energy as a wireless charging for the WSN [8]. However, even if the additional power can be harvested to the WSN, the resource is still limited for frequent use. Image compressing is a more detail strategy to reduce excessive energy consumption of WSN. There are many methods of image compressing used for this problem [9], [10]. This thesis uses the image compression strategy by creating DWT machine instruction to be inserted in DLX microprocessor so that the processor will run the specific instructions. This strategy will be efficient to get the most ideal microprocessor to be implanted in the WSN. Furthermore, the WSN will not be wasting energy on other microprocessor instructions that will be left unused. The purpose of this thesis is to analyze the energy efficiency WSN by reconstructing the machine instruction. The benefit is this method can be successfully implemented on WSN in multimedia sector. The problem can be formulated as how effective DWT for optimum machine instruction affects the WSN energy efficiency in multimedia monitoring system. This thesis uses DLX microprocessor and Haar DWT algorithm in DLX assembly language. The parameters for this paper are the compression result, the power consumption, and ISSN : 2355-9365 e-Proceeding of Engineering : Vol.6, No.2 Agustus 2019 | Page 3542 the speed of simulation. The completion of this thesis uses several methodologies, such as literature study, designing the system, and simulation.