Search Results for "design analysis algorithm"
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Showing 4 open source projects for "design analysis algorithm"
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google-java-format
Reformats Java source code to comply with Google Java Style
...The formatter can act on whole files, on limited lines, on specific offsets, passing through to standard-out (default) or altered in-place. There is no configurability as to the formatter's algorithm for formatting. This is a deliberate design decision to unify our code formatting on a single format. The formatter can be used in software that generates java to output more legible java code. Just include the library in your maven/gradle/etc. configuration. A google-java-format IntelliJ plugin is available from the plugin repository. ... -
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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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PetoronHash-System
PHASH | post-quantum XOF hashing algorithm | C++20
PHASH is a self-contained, dependency-free, post-quantum XOF hashing algorithm implemented in modern C++20. This release delivers the first fully stable production implementation of the PetoronHash-System — a 1600-bit sponge-based hash function with domain separation, extendable output, and deterministic behavior. Key Features No external dependencies — pure C++20 implementation. Extendable Output (XOF) — supports arbitrary output length (256–8192+ bits). -
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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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