Search Results for "cpu memory usage"
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Showing 7 open source projects for "cpu memory usage"
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MegEngine
Easy-to-use deep learning framework with 3 key features
...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 algorithm. Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.5 to 3.8. ... -
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bitnet.cpp
Official inference framework for 1-bit LLMs
bitnet.cpp is the official open-source inference framework and ecosystem designed to enable ultra-efficient execution of 1-bit large language models (LLMs), which quantize most model parameters to ternary values (-1, 0, +1) while maintaining competitive performance with full-precision counterparts. At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous... -
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MNN
MNN is a blazing fast, lightweight deep learning framework
...Android platform, core so size is about 400KB, OpenCL so is about 400KB, Vulkan so is about 400KB. Supports hybrid computing on multiple devices. Currently supports CPU and GPU. -
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Sming
Open source framework simplifying the creation of C++ applications
Sming is an asynchronous embedded C/C++ framework with superb performance and multiple network features. Sming is open source, modular and supports multiple architectures including ESP8266 and ESP32. Superb performance and memory usage (Sming compiles to native firmware!) Fast and user-friendly development. Integrated host emulator to assist with developing, testing, and debugging libraries and applications on a PC before uploading them to an actual microcontroller. Built-in powerful wireless modules. Compatible with standard Libraries use popular hardware in a few lines of code. ... -
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5
EnTT
A fast and reliable entity component system (ECS) and much more
...A constexpr utility for human readable resource names. An incredibly fast entity-component system based on sparse sets, with its own pay for what you use policy to adjust performance and memory usage according to the users' requirements. Offers a minimal configuration system built using the monostate pattern. -
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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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MathX
Fixed-length Arithmetic-types library
MathX is a fixed-length arithmetic-types written in pure c++ templates. The goal is to provide signed-integer, unsigned-integer, IEEE-754 float-point and fixed-point types, all with specific number of bits. To this moment, only signed-integer and unsigned-integer are completed for little-endain architecture. Any compiler that support c++03 or c++11 can successfully compile MathX. Refer to README for more information.
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