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Showing 12 open source projects for "android arm"
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MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity... -
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XNNPACK
High-efficiency floating-point neural network inference operators
XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. 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... -
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CPU Features
A cross platform C99 library to get cpu features at runtime
...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. The design emphasizes portability, extensibility, and compatibility with sandboxed or restricted environments where direct CPU access may be limited. -
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Halide
A language for fast, portable data-parallel computation
Halide is a programming language for fast, portable data-parallel computation. It was designed to make writing high-performance image and array processing code much easier on modern machines. It works on all major operating systems and with several CPU architectures (X86, ARM, MIPS, Hexagon, PowerPC) and GPU Compute APIs (CUDA, OpenCL, OpenGL, among others). It isn't a standalone programming language however; rather it is embedded in C++ which means that you write C++ code, building an... -
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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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Hippy
Tool to build cross-platform and high-performance awesome apps
...For Android, we recommend using the real cellphone for better develop experience, because Hippy is using X5 JS engine which can't support x86 simulator, as well as ARM simulator has a low performance. For iOS, we recommend to use iOS simulator when first try. However, you can change the Xcode configuration to install the app to iPhone if you are an iOS expert. -
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Blynk C++ Library
Blynk library for embedded hardware
Blynk is the most popular Internet of Things platform for connecting any hardware to the cloud, designing apps to control them, and managing your deployed products at scale. With Blynk Library you can connect over 400 hardware models (including ESP8266, ESP32, NodeMCU, all Arduinos, Raspberry Pi, Particle, Texas Instruments, etc.)to the Blynk Cloud. With Blynk apps for iOS and Android apps you can easily drag-n-drop graphic interfaces for any DIY or commercial project. It's a pure WYSIWG... -
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR... -
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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... -
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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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RapidJSON
A fast JSON parser/generator for C++ with both SAX/DOM style API
RapidJSON is a JSON parser and generator for C++. It was inspired by RapidXml. RapidJSON is small but complete. It supports both SAX and DOM style API. The SAX parser is only a half thousand lines of code. RapidJSON is fast. Its performance can be comparable to strlen(). It also optionally supports SSE2/SSE4.2 for acceleration. RapidJSON is self-contained and header-only. It does not depend on external libraries such as BOOST. It even does not depend on STL. RapidJSON is memory-friendly.... -
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Android on PXA270
Android on PXA270
Porting Android to Intel(Marvell) ARM XScale PXA270. A complete build system for embedded Linux and Android on PXA270. It was open source for anyone but no responsibility for any damage resulting from any usage of the software. Demo : http://www.youtube.com/watch?v=3Ine1k4Hzy8 http://www.youtube.com/watch?v=3Xp5zMPOI1c http://www.youtube.com/watch?
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