A division-free algorithm for fixed-point power exponential function in embedded system

Chang, Chung-Hsien; Chen, Shi-Huang; Chen, Bo-Wei; Wang, Jia-Ching; Wang, Jhing-Fa

doi:10.1109/icot.2013.6521197

Cited by 3 publications

(1 citation statement)

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“…When the compiler optimization performance was low, implementation techniques to avoid division were very useful in the field of embedded electronic circuits [24]. Even today, many MCUs do not have an extended instruction set, so such techniques are very important.…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Approximation Methods for Trigonometric and Arctangent Calculations for Low-Performance Computers

Kusaka¹,

Tanaka

2022

Electronics

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In modern computers, complicated signal processing is highly optimized with the use of compilers and high-speed processing using floating-point units (FPUs); therefore, programmers have little opportunity to care about each process. However, a highly accurate approximation can be processed in a small number of computation cycles, which may be useful when embedded in a field-programmable gate array (FPGA) or micro controller unit (MCU), or when performing many large-scale operations on a graphics processing unit (GPU). It is necessary to devise algorithms to obtain the desired calculated values without an accelerator or compiler assistance. The residual correction method (RCM) developed here can produce simple and accurate approximations of certain nonlinear functions with minimal multiply–add operations. In this study, we designed an algorithm for the approximate computation of trigonometric and inverse trigonometric functions, which are nonlinear elementary functions, to achieve their fast and accurate computation. A fast first approximation and a more accurate second approximation of each function were created using RCM with a less than 0.001 error using multiply–add operations only. This achievement is particularly useful for MCUs, which have a low power consumption but limited computational power, and the proposed approximations are candidate algorithms that can be used to stabilize the attitude control of robots and drones, which require real-time processing.

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Section: Introductionmentioning

confidence: 99%