YashasSamaga / ConvolutionBuildingBlocks
GEMM and Winograd based convolutions using CUTLASS
☆25Updated 4 years ago
Related projects ⓘ
Alternatives and complementary repositories for ConvolutionBuildingBlocks
- A Winograd Minimal Filter Implementation in CUDA☆23Updated 3 years ago
- ☆38Updated 4 years ago
- Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation☆26Updated 5 years ago
- CUDA templates for tile-sparse matrix multiplication based on CUTLASS.☆49Updated 6 years ago
- ☆48Updated 8 months ago
- SparseTIR: Sparse Tensor Compiler for Deep Learning☆131Updated last year
- ☆31Updated 2 years ago
- A Data-Centric Compiler for Machine Learning☆82Updated 10 months ago
- ☆67Updated last year
- Benchmark scripts for TVM☆73Updated 2 years ago
- Official implementation of "Searching for Winograd-aware Quantized Networks" (MLSys'20)☆27Updated last year
- Benchmark PyTorch Custom Operators☆13Updated last year
- ☆80Updated 7 months ago
- ☆40Updated 3 years ago
- Code for paper "Design Principles for Sparse Matrix Multiplication on the GPU" accepted to Euro-Par 2018☆71Updated 4 years ago
- ☆17Updated 3 years ago
- ☆17Updated 4 years ago
- Implementation of convolution layer in different flavors☆67Updated 7 years ago
- Test suite for probing the numerical behavior of NVIDIA tensor cores☆30Updated 3 months ago
- modified cutlass☆14Updated 4 years ago
- ☆14Updated last month
- Post-training sparsity-aware quantization☆33Updated last year
- Implementation of TSM2L and TSM2R -- High-Performance Tall-and-Skinny Matrix-Matrix Multiplication Algorithms for CUDA☆31Updated 4 years ago
- Memory Optimizations for Deep Learning (ICML 2023)☆60Updated 8 months ago
- MLIRX is now defunct. Please see PolyBlocks - https://docs.polymagelabs.com☆38Updated 11 months ago
- Benchmark for matrix multiplications between dense and block sparse (BSR) matrix in TVM, blocksparse (Gray et al.) and cuSparse.☆24Updated 4 years ago
- An extension library of WMMA API (Tensor Core API)☆84Updated 4 months ago
- ☆44Updated 5 years ago
- A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores☆43Updated 11 months ago
- Automatic Schedule Exploration and Optimization Framework for Tensor Computations☆176Updated 2 years ago