#
neon
Here are 246 public repositories matching this topic...
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
-
Updated
Jul 30, 2021 - C++
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
-
Updated
Dec 23, 2020 - Python
The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.
android
linux
machine-learning
arm
computer-vision
neural-network
cpp
neon
opencl
simd
armv7
aarch64
armv8
sve
-
Updated
Jul 13, 2021 - C++
C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM.
c-plus-plus
machine-learning
arm
neural-network
neon
image-processing
avx
sse
simd
avx2
sse2
sse41
avx512
powerpc
altivec
vsx
ssse3
simd-library
haar-cascade
lbp
-
Updated
Jul 28, 2021 - C++
Open
Relaxed SIMD support
nemequ
commented
Jul 11, 2021
The WebAssembly people are working on a relaxed SIMD proposal which mostly just provides alternatives for already-implemented functions, but allows for some differences between different implementations (e.g., allowing different results for out-of-range values, NaNs, etc.).
This should be pretty easy issue to resolve; we can mostly just copy the
C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, NEON, AVX512)
cpp
neon
c-plus-plus-11
avx
sse
simd
vectorization
avx512
mathematical-functions
simd-instructions
simd-intrinsics
-
Updated
Jul 30, 2021 - C++
SIMD Vector Classes for C++
c-plus-plus
cpp
portable
neon
cpp14
parallel
parallel-computing
avx
sse
cpp11
simd
cpp17
avx2
simd-programming
vectorization
avx512
simd-instructions
simd-vector
data-parallel
-
Updated
Jul 8, 2021 - C++
c
euler
opengl
math
postfix
neon
vector
matrix
bezier
avx
sse
simd
affine-transform-matrices
opengl-math
3d
bounding-boxes
matrix-decompositions
frustum
3d-math
marix-inverse
glm-for-c
-
Updated
Jun 15, 2021 - C
b-zee
commented
Mar 17, 2021
Native Go version of HighwayHash with optimized assembly implementations on Intel and ARM. Able to process over 10 GB/sec on a single core on Intel CPUs - https://en.wikipedia.org/wiki/HighwayHash
-
Updated
Mar 25, 2021 - Go
-
Updated
Jul 28, 2021 - PHP
C++ SIMD Noise Library
neon
simplex
fractal
sse
simd
noise
cellular
avx2
perlin
perlin-noise
white-noise
noise-library
noise-3d
fastnoise-simd
simplex-noise
fastnoise
-
Updated
Mar 20, 2021 - C++
A translator from Intel SSE intrinsics to Arm/Aarch64 NEON implementation
arm
neon
sse
simd
x86
arm64
aarch64
armv8
armv7l
intel-intrinsics
biilabs
armv8-a
intel-sse-intrinsics
apple-silicon
neon-intrinsics
sse-intrinsics
sse2neon
-
Updated
Jul 29, 2021 - C
Performance-portable, length-agnostic SIMD with runtime dispatch
-
Updated
Jul 30, 2021 - C++
SIMD Library for Evaluating Elementary Functions, vectorized libm and DFT
android
ios
arm
neon
cuda
avx
simd
elementary-functions
sse2
fft
vectorization
math-library
aarch64
avx512
powerpc
vsx
vector-math
s390x
quadruple-precision
sve
-
Updated
Jul 9, 2021 - C
-
Updated
Jul 20, 2021
Fast integer compression in C using the StreamVByte codec
-
Updated
Jul 5, 2021 - C
Math library using hlsl syntax with SSE/NEON support
math
cpp
shaders
neon
c-plus-plus-11
vector
matrix
modern-cpp
game-development
avx
sse
quaternion
variants
hlsl
sse41
math-library
ser
-
Updated
May 3, 2021 - C++
Fast inference engine for Transformer models
deep-neural-networks
cpp
neon
openmp
parallel-computing
cuda
avx
intrinsics
avx2
neural-machine-translation
opennmt
quantization
gemm
mkl
thrust
transformer-models
onednn
-
Updated
Jul 30, 2021 - C++
Agenium Scale vectorization library for CPUs and GPUs
hpc
neon
cuda
avx
simd
avx2
sse2
simd-programming
aarch64
avx512
simd-instructions
simd-library
sse42
rocm
cpp20
sve
neon128
cpp20-library
vectorization-library
-
Updated
Jul 29, 2021 - Python
Improve this page
Add a description, image, and links to the neon topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the neon topic, visit your repo's landing page and select "manage topics."
Though we include Boost JSON as a dependency for benchmarking purposes, we do not include it as part of our benchmarks currently.