Gpu Acceleration
Showing 14 of 158 projects
A compact spiking neural network library built on JAX and Haiku, offering high-performance training via surrogate gradient descent and neuroevolution.
A pure Go, GPU-accelerated 2D graphics library for building IDEs, browsers, and graphics-intensive applications.
A deep belief net and deep learning implementation written in F# with GPU acceleration via Alea.cuBase.
A GPU-accelerated (CUDA) C++ template library for building and training artificial neural networks, including self-organizing maps and back-propagation networks.
An ActionScript 3 library for parsing OpenType fonts and rendering them with GPU acceleration via Stage3D.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
A pre-configured Docker image with deep learning frameworks, data science tools, and GPU support for rapid environment setup.
A modular MATLAB-based platform for analyzing super-resolution microscopy (SMLM) data with GPU-accelerated fitting.
A learning-focused, high-performance tensor computation library built from scratch in Rust with automatic differentiation and CPU/CUDA backends.
A tiny GPU-based UI library for Stage3D that renders the entire interface in a single draw call.
Kernex extends JAX with kmap and kscan for differentiable stencil computations, enabling efficient array transformations.
A Deno module for matrix, ndarray, and tensor operations accelerated by WebGPU and WASM.
A lightweight deep learning library written in C++ with C, C#, and Python interfaces, supporting CPU and GPU computation.
GPU/TPU accelerated nonlinear least-squares curve fitting using JAX, designed as a drop-in replacement for SciPy's curve_fit.
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