2 dépôts
Awesome GitHub RepositoriesWeb-Based Math Accelerators
High-performance engines that leverage browser-based graphics hardware for mathematical and matrix operations.
Distinct from Hardware-Accelerated WebGL Execution: Distinct from Hardware-Accelerated WebGL Execution: focuses on the math acceleration engine aspect rather than general WebGL performance.
Explore 2 awesome GitHub repositories matching web development · Web-Based Math Accelerators. Refine with filters or upvote what's useful.
Awesome Web-Based Math Accelerators GitHub Repositories
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- gpujs/gpu.js
gpujs/gpu.js
15,377Voir sur GitHubThis library is a JavaScript framework for general-purpose computing on graphics processing units. It enables the execution of parallel mathematical operations directly within the browser by offloading data-heavy calculations to graphics hardware. The project functions as a web-based math accelerator that converts standard JavaScript functions into shader code for execution on the graphics processor. It provides a unified interface that detects available graphics APIs and manages data transfer between system and graphics memory. To ensure compatibility across diverse environments, the library
Functions as a high-performance engine for running complex matrix and vector operations in the browser.
JavaScriptglslgpgpugpu - webdevsimplified/face-detection-javascript
WebDevSimplified/Face-Detection-JavaScript
1,036Voir sur GitHubThis project is a JavaScript library designed for real-time face detection directly within a web browser. It functions as a machine learning model wrapper that enables developers to identify and track human faces in live video streams without the need for backend server processing. The library utilizes browser-native media access to stream raw camera data into application memory, where it performs pixel-level analysis. By leveraging a tensor-based inference engine and web-assembly acceleration, the tool executes complex neural network calculations locally to achieve high-performance computer
Executes heavy mathematical operations for neural network calculations using low-level binary instructions to achieve near-native performance within the browser environment.
JavaScript
