Ne10 v1.2.0 is released. Now radix-3 and radix-5 are supported in floating point complex FFT. Benchmark data below shows that NEON optimization has significantly improved performance of FFT.

# 1. Project Ne10

The Ne10 project has been set up to provide a set of common, useful functions which have been heavily optimized for the ARM Architecture and provide consistent well tested behavior that can be easily incorporated into applications. C interfaces to the functions are provided for both assembler and NEON™ implementations. The library supports static and dynamic linking and is modular, so that functionality that is not required can be discarded. For details of Ne10, please check this blog. For more details of FFT feature in Ne10, please refer this blog.

# 2. Benchmark

## 2.1. Time cost

Figure 1 is benchmark data (time cost) of four FFT implementations, including Ne10 (v1.2.0), pffft (2013), kissFFT (1.3.0), and one inside Opus (v1.1.1-beta). Ne10 and pffft are well NEON-optimized, while kissFFT and Opus FFT are not. All implementations are compiled by LLVM 3.5, with -O2 flag. All these implementations have been tested on ARM v7-A (Cortex-A9, 1.0GHz) and AArch64 (Cortex-A53, 850MHz).

Figure 1

In figure 1, x axis is size of FFT and y axis is time cost (ms), smaller is better. Each FFT has been run for 2.048x10^{6 }/ (size of FFT) times. Say, we run 2000 times for 1024 points FFT. Only multiple of 16 sizes are supported in pffft, so its curve starts from 240. Performance boost after NEON optimization is obvious.

## 2.2. Mega Floating-point operations per second (MFLOPS)

Figure 2

Figure 2 is benchmark data in MFLOPS of these four implementations. Data are calculated according to this link. MFLOPS is a measure of performance of different algorithms in solving the same problem, bigger is better. When data are packed and processed by NEON instructions (in Ne10 and Pffft), MFLOPS is much higher.

## 3. Usage

API of FFT is not modified. Ne10 detects whether the size of FFT is multiple of 3 or 5, and then selects the best algorithms to execute. For more detail, please refer this blog.

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