quettabit / convolution_kernel
Accelerating CNN's convolution operation on GPUs by using memory-efficient data access patterns.
☆14Updated 6 years ago
Related projects ⓘ
Alternatives and complementary repositories for convolution_kernel
- Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation☆26Updated 5 years ago
- Example for applying Gaussian and Laplace clipping on activations of CNN.☆34Updated 5 years ago
- Yet another Polyhedra Compiler for DeepLearning☆19Updated last year
- This is the implementation for paper: AdaTune: Adaptive Tensor Program CompilationMade Efficient (NeurIPS 2020).☆13Updated 3 years ago
- Benchmark scripts for TVM☆73Updated 2 years ago
- ☆67Updated last year
- ICML2017 MEC: Memory-efficient Convolution for Deep Neural Network C++实现(非官方)☆17Updated 5 years ago
- source code of the paper: Robust Quantization: One Model to Rule Them All☆37Updated last year
- I'm going to use the Winograd’s minimal filtering algorithms to introduce a new class of fast algorithms for convolutional neural networks…☆12Updated 6 years ago
- This repository represents training examples for the CVPR 2018 paper "SYQ:Learning Symmetric Quantization For Efficient Deep Neural Netwo…☆32Updated 5 years ago
- Test winograd convolution written in TVM for CUDA and AMDGPU☆40Updated 6 years ago
- This repository containts the pytorch scripts to train mixed-precision networks for microcontroller deployment, based on the memory contr…☆49Updated 6 months ago
- ☆36Updated 5 years ago
- ☆46Updated 4 years ago
- ☆13Updated 4 years ago
- TVM stack: exploring the incredible explosion of deep-learning frameworks and how to bring them together☆63Updated 6 years ago
- TQT's pytorch implementation.☆20Updated 2 years ago
- implementation of winograd minimal convolution algorithm on Intel Architecture☆39Updated 6 years ago
- Post-training sparsity-aware quantization☆33Updated last year
- Benchmark for matrix multiplications between dense and block sparse (BSR) matrix in TVM, blocksparse (Gray et al.) and cuSparse.☆24Updated 4 years ago
- ☆55Updated 3 years ago
- PyTorch -> ONNX -> TVM for autotuning☆23Updated 4 years ago
- ☆17Updated 3 years ago
- GEMM and Winograd based convolutions using CUTLASS☆25Updated 4 years ago
- Official implementation of "Searching for Winograd-aware Quantized Networks" (MLSys'20)☆27Updated last year
- Implementation of the Winograd algorithm.☆22Updated 6 years ago
- ☆38Updated 4 years ago
- GPU implementation of Winograd convolution☆10Updated 7 years ago
- Pytorch implementation for FAT: learning low-bitwidth parametric representation via frequency-aware transformation☆27Updated 3 years ago