Optimizing core signal processing functions on a superscalar SIMD architecture

Download

index.pdf

Date

2019

Author

Uslu, Çağrı

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

156
views

81
downloads

Digital Signal Processing (DSP) is the basis of many technologies, such as Image Processing, Speech Recognition, Radars, etc. Use of electronic devices such as smart- phones, smartwatches, self-driving cars and autonomous robots that take advantage of these technologies becomes widespread and hence it is more critical than ever for these technologies to be realized with high efficiency on cheaper and less power- hungry devices. Cortex-A15 processor architecture is one of the solutions from ARM to this requirement. Therefore, it is worth to optimize certain DSP functions on the Cortex-A15. In this thesis, four commonly used DSP operations are implemented on an ARM Cortex-A15 processor, heavily utilizing the vector co-processor NEON. The optimized operations are Matrix Addition, Matrix Multiplication, Convolution, and Fourier Transform. Although numerous DSP libraries implement these operations, they are not tailored to a specific processor. The functions implemented in this thesis aim to be most efficient on Cortex-A15, which is a superscalar, out-of-order executing processor. All types of processors may suffer from pipeline stalls. However, unlike scalar processors, superscalar processors may achieve a superscalar performance even in the presence of pipeline stalls. This could be accomplished by utilizing the execution units of the processor better. One way of possibly increasing the utilization of the execution units is instruction reordering. To reorder instructions optimally, one must know certain specifications of the architecture. To discover one of those specifications, i.e. the cost of instructions in clock cycles, a method is developed for performing the appropriate time measurements. Additionally, a set of guidelines for instruction reordering is conceived. Using these guidelines, among other optimization techniques, the DSP functions mentioned earlier are manually optimized to achieve a high execution performance.

Subject Keywords

Signal processing, Signal processing Digital techniques, Keywords: ARM, NEON, SIMD, Optimization, Instruction Reordering, Digital Signal Processing

URI

http://etd.lib.metu.edu.tr/upload/12623693/index.pdf
https://hdl.handle.net/11511/43930

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A Comparison of sparse signal recovery and approximate bayesian inference methods for sparse channel estimation Uçar, Ayla; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015) The concept of sparse representation is one of the central methodologies of modern signal processing and it has had significant impact on numerous application fields such as communications and imaging. Sparsity expresses the idea that the information rate of a continuous time signal may be much smaller than suggested by its bandwidth, or that a discrete time signal depends on a number of degrees of freedom which is comparably much smaller than its (finite) length. With recent advances in sparse signal estim...
SNR CALCULATION METHOD FOR REMOTE SENSING SATELLITE IMAGING SYSTEMS Turkmenoglu, Mustafa; Sengul, Orhan; Demircioglu, Erdem (2013-06-01) Signal to Noise Ratio (SNR) is a metric used to link the image quality and radiometric performance of the remote sensing imaging systems. It is one of the remote sensing imaging system's design parameters that represents the image quality. SNR calculation and analysis should be carried out at design phase of remote sensing imaging systems. This calculation and analysis are crucial for confirmation of design success. It is important to show that the light flux reaching the sensor and the generated electrons ...
Application of F-test method on model order selection and related problems Yazar, Alper; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015) Signal modeling is one of the important topics of signal processing area. The input signal should be modeled with a suitable mathematical model first. In statistics related disciplines, there are information theory based criteria for model order selection topic. In this thesis work, F-test based methods are proposed on model order selection and related problems. F-test is used in statistics related disciplines. However, it is not so widely used in signal processing related problems. Solution approaches for ...
Properly Handling Complex Differentiation in Optimization and Approximation Problems Candan, Çağatay (Institute of Electrical and Electronics Engineers (IEEE), 2019-03-01) Functions of complex variables arise frequently in the formulation of signal processing problems. The basic calculus rules on differentiation and integration for functions of complex variables resemble, but are not identical to, the rules of their real variable counterparts. On the contrary, the standard calculus rules on differentiation, integration, series expansion, and so on are the special cases of the complex analysis with the restriction of the complex variable to the real line. The goal of this lect...
Phase sensitive detection of motor fault signatures in the presence of noise Akin, Bilal; Toliyat, Hamid; Orguner, Umut; Rayner, Mark (2007-05-05) In this paper, digital signal processor (DSP)-based phase-sensitive motor fault signature detection is presented. The implemented method has a powerful line current noise suppression capability while detecting the fault signatures. Because the line current of inverter driven motors involve low order harmonies, high frequency switching disturbances, and the noise generated by harsh industrial environment; the real-time fault analyses yield erroneous or fluctuating fault signatures. This situation becomes a s...

Citation Formats

Ç. Uslu, “Optimizing core signal processing functions on a superscalar SIMD architecture,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.