Search Results for "neon"
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Showing 15 open source projects for "neon"
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Synthwave ’84 (VSCode Theme)
Synthwave inspired colour theme for VS Code
...The theme also includes optional “neon glow” effects on UI elements, cursor styles, and sidebars for users who want a more immersive style. Many users choose it to inject personality into their development environment—especially in late-night coding sessions. Because it’s just a theme, it can be layered with other UI tweaks or paired with complementary icon packs and UI themes in VS Code. -
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SIMD
C++ wrappers for SIMD intrinsics
...However, differences across CPU architectures and compilers make direct usage complex, which xsimd addresses by offering a unified API that maps efficiently to underlying hardware capabilities. The library supports a wide range of instruction sets, including SSE, AVX, NEON, and WebAssembly SIMD, ensuring portability across platforms. It provides vectorized implementations of common mathematical operations, allowing developers to operate on batches of values using familiar syntax. xsimd is widely adopted in performance-critical applications. -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including... -
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HLSL++
Math library using HLSL syntax with multiplatform SIMD support
...It provides vector, matrix, and math operations with a syntax identical or very similar to HLSL, allowing seamless transition between shader code and application code. The library is optimized for performance and supports SIMD instructions across multiple architectures, including SSE, AVX, AVX2, AVX512, and ARM NEON, ensuring high efficiency on modern hardware. It also extends beyond standard HLSL capabilities by introducing additional features such as quaternion support, advanced matrix operations, and extended vector types like float8. The library is particularly valuable for game developers who need consistency between CPU and GPU computations, reducing errors and improving maintainability. -
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XNNPACK
High-efficiency floating-point neural network inference operators
...Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. The library is written in C/C++ and designed for maximum portability, efficiency, and performance, leveraging platform-specific instruction sets (e.g., NEON, AVX, SIMD) for optimized execution. It supports NHWC tensor layouts and allows flexible striding along the channel dimension to efficiently handle channel-split and concatenation operations without additional cost. -
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CPU Features
A cross platform C99 library to get cpu features at runtime
cpu_features is a cross-platform C library developed by Google that provides a simple and efficient way to detect available CPU features at runtime across a wide range of architectures and operating systems. It enables applications to determine which instruction sets (such as SSE, AVX, or NEON) are supported on the host machine, allowing developers to optimize performance dynamically. The library supports numerous architectures—including x86, ARM, AArch64, MIPS, POWER, RISCV, LoongArch, and s390x—and works on major operating systems like Linux, macOS, Windows, FreeBSD, Android, and iOS. Implemented in portable C99, it is thread-safe, has no memory allocations, and raises no exceptions, making it suitable even for use in low-level system libraries. ... -
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UniSIMD-assembler
SIMD macro assembler unified for ARM, MIPS, PPC and x86
...The assembler itself isn't a separate tool, but rather a collection of C/C++ header files, which applications need to include directly in order to use. At present, Intel SSE/SSE2/SSE4 and AVX/AVX2/AVX-512 (32/64-bit x86 ISAs), ARMv7 NEON/NEONv2, ARMv8 AArch32 and AArch64 NEON, SVE (32/64-bit ARM ISAs), MIPS 32/64-bit r5/r6 MSA and POWER 32/64-bit VMX/VSX (little/big-endian ISAs) are mostly implemented (/w horizontal reductions) although scalar improvements, wider SIMD vectors with zeroing/merging predicates in 3/4-operand instructions are planned as extensions to current 2/3-operand SPMD-driven vertical SIMD ISA. ... -
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libjpeg-turbo
SIMD-accelerated libjpeg-compatible JPEG codec library
libjpeg-turbo is a JPEG image codec that uses SIMD instructions (MMX, SSE2, NEON, AltiVec) to accelerate baseline JPEG compression and decompression on x86, x86-64, ARM, and PowerPC systems. On such systems, libjpeg-turbo is generally 2-6x as fast as libjpeg, all else being equal. On other types of systems, libjpeg-turbo can still outperform libjpeg by a significant amount, by virtue of its highly-optimized Huffman coding routines. -
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TurboPFor
Fastest Integer Compression
Fastest Integer Compression. ALL functions are available for AMD/Intel, 64-bit ARMv8 NEON Linux+MacOS/M1 & Power9 Altivec. 100% C (C++ headers), as simple as memcpy. OS:Linux amd64, arm64, Power9, MacOs (Amd/intel + Apple M1). -
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Eclipse Portable [4.6–4.26]
Portable version of the multifunctional development platform Eclipse.
With this portable version of Eclipse, you can develop software directly from your removable mass storage device, even without admin rights. To get started, please visit the Eclipse Portable wiki: https://sourceforge.net/p/eclipse-neon-portable/wiki GitHub: https://github.com/Berny23/eclipse-portable Thank you for over 180,000 Downloads! Please note: A few months ago, I completely switched to Linux (now Arch btw). So, I'll be no longer active creating portable apps for Windows. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes obligatory when running a model. Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. ... -
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libfacedetection
Library for face detection in images
...You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler. SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM. The model file has also been provided in directory ./models/. The file examples/detect-image.cpp and examples/detect-camera.cpp show how to use the library. The library was trained by libfacedetection.train. You can copy the files in directory src/ into your project, and compile them as the other files in your project. The source code is written in standard C/C++. ... -
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Simd
High performance image processing library in C++
...In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows, Android and Linux, MSVS, G++ and Clang compilers, MSVS project and CMake build systems. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. ... -
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