Search Results for "energy"

...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 compute infrastructure. The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources.

Cactus is a low-latency, energy-efficient AI inference framework designed specifically for mobile devices and wearables, enabling advanced machine learning capabilities directly on-device. It provides a full-stack architecture composed of an inference engine, a computation graph system, and highly optimized hardware kernels tailored for ARM-based processors. Cactus emphasizes efficient memory usage through techniques such as zero-copy computation graphs and quantized model formats, allowing large models to run within the constraints of mobile hardware. ...

Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is available both for Unix and Windows platforms (a dedicated platform archive is available on request). ...

Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency, and energy. One instance of where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.These algorithms can train models for classical supervised learning problems with memory requirements that are orders of magnitude lower than other modern ML algorithms. ...

An open-source C++ library of machine learning by New York University's machine learning lab, led by Yann LeCun. In particular, implementations of convolutional neural networks with energy-based models along with a GUI, demos and tutorials.

The April project defines a set of C++ objects (World, Actor, Sensor, Reflex, Brain, Actuator, Event) that help simulate various environments. The world has an amount of energy that the user may use to create Actors and components while Actors may also create other Actors from their own energy. The code is structured in several layers: - april-core simply defines the library and the objects - april-gui builds on the concepts in april-core, giving a visual interpretation to the concepts - AprilDream is a GUI program that uses april-gui objects to display a World in evolution. ...

The Local Binary Pattern (LBP) is a texture operator that is used in several different computer vision applications and implemented in a variety of platforms. When selecting a suitable LBP implementation platform, the specific application and its requirements in terms of performance, size, energy efficiency, cost and developing time has to be carefully considered. This is a software toolbox that collects software implementations of the Local Binary Pattern operator in several platforms: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. ...

IMPORTANT UPDATE! This project has migrated to https://github.com/polyworld/polyworld . This SourceForge repository is now obsolete. Polyworld is one of the earliest and most sophisticated artificial worlds developed to study Artificial Life and Artificial Intelligence, using computational genetics, physiology, metabolism, neural networks, learning, vision, and behavior.

Surviving bots is continuous software application in Java that will guide robots to search for energy source around it, tap it and explore ahead for survival and thus evolve into much more advance system.Software is ment to be hardware independent.