5 dépôts
Methods for reducing the computational cost of convolutions by decomposing high-dimensional kernels.
Distinct from One-Dimensional Convolutions: Existing candidates focus on 1D convolutions or hardware caching, not mathematical kernel decomposition.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Convolutional Kernel Optimizations. Refine with filters or upvote what's useful.
This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i
Explains how to represent multi-dimensional kernels as series of 1D convolutions to reduce computation.
TileLang is a Python-embedded domain-specific language compiler that JIT-compiles and autotunes GPU kernels. It uses a tile-based DSL, automatic software pipelining, and parallel autotuning to generate optimized GPU kernels at runtime. It supports tensor core operations with Pythonic syntax, automatic memory management, and thread mapping. The compiler searches over tile sizes, thread counts, and scheduling policies, compiling and benchmarking candidates in parallel to find the fastest kernel. It also caches compiled binaries and tuning results to disk for reuse across sessions. TileLang inc
Defines matrix-matrix convolution computations with configurable dimensions, data types, and optional bias.
PlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural
Provides Poly Kernel Inception Blocks that process features through parallel depthwise convolutions with varying dilations.
This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen
Renders weight tensors from convolutional layers as images to analyze learned patterns.
Lenia is a scientific research tool and computational framework for modeling, visualizing, and analyzing artificial life. It functions as a continuous cellular automata engine that simulates emergent biological patterns using continuous space-time grids and kernel-based convolutional dynamics. The system leverages a GPU-accelerated simulation framework to perform parallel tensor computations and massive grid updates in real time. It includes hardware-accelerated 3D rendering to visualize high-dimensional cellular structures and their structural complexity. The platform provides capabilities
Determines cellular evolution by calculating the weighted influence of neighboring cells through mathematical growth functions.