puckbee

puckbee's Stars

• kelseyhightower/nocode

  The best way to write secure and reliable applications. Write nothing; deploy nowhere.

  Language:Dockerfile61.3k3644.7k4.7k

• janishar/mit-deep-learning-book-pdf

  MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

  Language:Java13k411182.7k

• Theano/Theano

  Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor

  Language:Python9.9k5392.7k2.5k

• aaron-xichen/pytorch-playground

  Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

  Language:Python2.6k5350614

• jbush001/NyuziProcessor

  GPGPU microprocessor architecture

  Language:C2k142168356

• fengbintu/Neural-Networks-on-Silicon

  This is originally a collection of papers on neural network accelerators. Now it's more like my selection of research on deep learning and computer architecture.

  1.9k3003383

• flame/how-to-optimize-gemm

  Language:C1.8k4418354

• Maratyszcza/NNPACK

  Acceleration package for neural networks on multi-core CPUs

  Language:C1.7k101196313

• SVF-tools/SVF

  Static Value-Flow Analysis Framework for Source Code

  Language:C++1.5k56613440

• Rock-100/FaceKit

  [CVPR 2018] Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

  Language:C++1.1k69100300

• NJU-ProjectN/nemu

  NJU EMUlator, a full system x86/mips32/riscv32/riscv64 emulator for teaching

  Language:C9391869198

• andravin/wincnn

  Winograd minimal convolution algorithm generator for convolutional neural networks.

  Language:Python6103027145

• iBreaker/book

  收集专业书籍 <欢迎提交>

  491160191

• LvNA-system/labeled-RISC-V

  Language:Scala16916141

• cdl-saarland/rv

  RV: A Unified Region Vectorizer for LLVM

  Language:C++107174416

• seung-lab/znn-release

  Multi-core CPU implementation of deep learning for 2D and 3D sliding window convolutional networks (ConvNets).

  Language:C++94335533

• elongbug/llvm-cookbook

  llvm-cookbook samples

  Language:Makefile793140

• EBD-CREST/nsparse

  Sparse matrix computation library for GPU

  Language:Cuda546312

• KastnerRG/spector

  Spector: An OpenCL FPGA Benchmark Suite

  Language:Shell4412417

• tanakamura/instruction-bench

  instruction-bench

  Language:C++35714

• arbenson/fast-matmul

  Fast matrix multiplication

  Language:C++296118

• karrenberg/wfv

  IMPORTANT NOTICE: This implementation is long outdated. The new libwfv will be released soon. Whole-Function Vectorization is an algorithm that transforms a scalar function in such a way that it computes W executions of the original code in parallel using SIMD instructions (W is the target architecture's SIMD width). This implementation of the algorithm is a language- and platform-independent code transformation that works on low-level intermediate code given by an arbitrary control-flow graph in SSA form (LLVM bitcode).

  Language:C++22405

• jszhujun2010/Clang-Basic-Tutorial

  Basic Clang library, LibTooling and Plugin

  Language:C++17237

• davidebarbieri/spgpu

  spGPU library for sparse linear algebra on GPUs

  Language:Cuda9201

• canercandan/linear-algebra

  A linear algebra framework in C++ along with a layout abstraction for parallelization paradigms. It provides operators to compute dense and sparse matrices with generically designed scalar, complex, vector and matrix types. At this time, the framework supports the libraries CUDA, CUBLAS, CUSP, CUSPARSE for parallel computing on GPGPU.

  Language:C++5403

• eerbil/Code-Selection-For-SpMV-Using-Deep-Learning

  Reimplementation of the paper "A Code Selection Mechanism Using Deep Learning" in Python.

  Language:Python4200

• kevinzhang334455/Scout

  UR research Project

  Language:C++3

• ryanh3nry/BarrettCUDA

  BarrettCUDA is a fast(ish) implementation of finite field sparse matrix-vector multiplication (SpMV) for Nvidia GPU devices, written in CUDA C++. BarrettCUDA supports SpMV for matrices expressed in the 'compressed column storage' (CCS) sparse matrix representation over (i) the field of integers modulo an arbitrary multi-precision prime, or (ii) either of the binary fields GF(2^8) or GF(2^16).

  3

• AdamHarries/sparseharness

  A harness/set of harnesses for executing spmv based algorithms from Lift

  Language:C++11

• SumithraSriram/Sparse-Matrices

  Implementation of Sparse Matrix Vector Multiplication using various Sparse Matrix storage formats

  Language:C++10