neon | 易学教程

sse/avx equivalent for neon vuzp

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问题 Intel's vector extensions SSE, AVX, etc. provide two unpack operations for each element size, e.g. SSE intrinsics are _mm_unpacklo_* and _mm_unpackhi_* . For 4 elements in a vector, it does this: inputs: (A0 A1 A2 A3) (B0 B1 B2 B3) unpacklo/hi: (A0 B0 A1 B1) (A2 B2 A3 B3) The equivalent of unpack is vzip in ARM's NEON instruction set. However, the NEON instruction set also provides the operation vuzp which is the inverse of vzip . For 4 elements in a vector, it does this: inputs: (A0 A1 A2 A3

Alignment in VLD1

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问题 I have a question about ARM Neon VLD1 instruction's alignment. How does the alignment in the following code work? DATA .req r0 vld1.16 {d16, d17, d18, d19}, [DATA, :128]! Does the starting address of this read instruction shifts to DATA + a positive integer, such that it is the smallest multiple of 16(16 bytes = 128 bits) which is no less than DATA, or DATA itself changes to the smallest multiple of 16 no less than DATA? 回答1: It is a hint to the CPU. Only thing I read about the usefulness of

编译且移植FFTW3到Android手机上(1)

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本文主要对如何将FFTW3编译且移植到Android App上进行介绍，同时对各FFTW提供的一些快速傅里叶变换的方法在手机进行性能测试，总结出使用FFTW3进行小规模傅里叶变换的最佳方式。文章重点内容有：FFTW configure；编译so库；ARM NEON优化；float加速；多线程第1部分为快速入门版，如想查看更详细的使用说明，请查看第2部分 : http://he-kai.com/?p=38 内容准备工作： FFTW 3：Version 3.3.3 http://fftw.org/fftw-3.3.3.tar.gz Android App：我编写的示例程序 https://github.com/hekai/fftw_android 使用git或直接去github上下载此项目 Linux：我使用的是Ubuntu 12.04 64-bit NDK：android-ndk-r9c 64-bit 32-bit 点击合适版本下载 Eclipse：我使用的版本是3.7，版本不同关系不大，关键是安装好 ADT 插件，并在Eclipse中配置好SDK目录和NDK目录快速入门： 1. 下载好 fftw-3.3.3.tar.gz 和 fftw_android 项目后，将目录结构按如下方式放置 —->parent folder —->fftw-3.3.3 —->fftw

C versus vDSP versus NEON - How could NEON be as slow as C?

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问题 How could NEON be as slow as C? I have been trying to build a fast Histogram function that would bucket incoming values into ranges by assigning them a value - which is the range threshold they are closest to. This is something that would be applied to images so it would have to be fast (assume an image array of 640x480 so 300,000 elements) . The histogram range numbers are multiples (0,25,50,75,100) . Inputs would be float and final outputs would obviously be integers I tested the following

On iOS how to quickly convert RGB24 to BGR24?

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问题 I use vImageConvert_RGB888toPlanar8 and vImageConvert_Planar8toRGB888 from Accelerate.framework to convert RGB24 to BGR24, but when the data need to transform is very big, such as 3M or 4M, the time need to spend on this is about 10ms. So some one know some fast enough idea?.My code like this: - (void)transformRGBToBGR:(const UInt8 *)pict{ rgb.data = (void *)pict; vImage_Error error = vImageConvert_RGB888toPlanar8(&rgb,&red,&green,&blue,kvImageNoFlags); if (error != kvImageNoError) { NSLog(@

Explaining ARM Neon Image Sampling

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问题 I'm trying to write a better version of cv::resize() of the OpenCV, and I came a cross a code that is here: https://github.com/rmaz/NEON-Image-Downscaling/blob/master/ImageResize/BDPViewController.m The code is for downsampling an image by 2 but I can not get the algorithm. I would like first to convert that algorithm to C then try to modify it for Learning purposes. Is it easy also to convert it to downsample by any size ? The function is: static void inline resizeRow(uint32_t *dst, uint32_t

Battery Power Consumption between C/Renderscript/Neon Intrinsics — Video filter (Edgedetection) APK

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问题 I have developed 3 C/RS/Neon-Intrinsics versions of Video Processing Algorithm using Android NDK (using C++ APIs for Renderscript). Calls to C/RS/Neon will be made to Native level on NDK side from JAVA front end. I found that for some reason Neon version consumes lot of power in comparison with C and RS versions. I used Trepn 5.0 for my power testing. Can some one clarify me regarding the power consumption level for each of these methods C , Renderscript - GPU, Neon Intrinsics. Which one

determinant calculation with SIMD

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问题 Does there exist an approach for calculating the determinant of matrices with low dimensions (about 4), that works well with SIMD (neon, SSE, SSE2)? I am using a hand-expansion formula, which does not work so well. I am using SSE all the way to SSE3 and neon, both under linux. The matrix elements are all floats. 回答1: Here's my 5 cents. determinant of a 2x2 matrix: that's an exercise for the reader, should be simple to implement determinant of a 3x3 matrix: use the scalar triple product. This

Efficiently compute max of an array of 8 elements in arm neon

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问题 How do I find max element in array of 8 bytes, 8 shorts or 8 ints? I may need just the position of the max element, value of the max element, or both of them. For example: unsigned FindMax8(const uint32_t src[8]) // returns position of max element { unsigned ret = 0; for (unsigned i=0; i<8; ++i) { if (src[i] > src[ret]) ret = i; } return ret; } At -O2 clang unrolls the loop but it does not use neon, which should give decent perf boost (because it eliminates many data dependent branches?) For

Add all elements in a lane

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问题 Is there an intrinsic which allows one to add all of the elements in a lane? I am using Neon to multiply 8 numbers together, and I need to sum the result. Here is some paraphrased code to show what I'm currently doing (this could probably be optimised): int16_t p[8], q[8], r[8]; int32_t sum; int16x8_t pneon, qneon, result; p[0] = some_number; p[1] = some_other_number; //etc etc pneon = vld1q_s16(p); q[0] = some_other_other_number; q[1] = some_other_other_other_number; //etc etc qneon = vld1q