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Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks

Chen Zhang, Zhenman Fang, Peipei Zhou, Peichen Pan, Jason Cong

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Tags: Caffe, Computer science, CUDA, Deep learning, DSP, FPGA, Neural networks, nVidia, Tesla K40

September 8, 2016 by hgpu

Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs

Chao Li, Yi Yang, Min Feng, Chakradhar Srimat, Huiyang Zhou

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X

September 5, 2016 by hgpu

DeepPy: Pythonic deep learning

Anders Boesen Lindbo Larsen

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python

September 3, 2016 by hgpu

Deep Learning on FPGAs

Griffin James Lacey

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Tags: Computer science, Deep learning, FPGA, nVidia, nVidia Quadro K600, OpenCL, Thesis

August 31, 2016 by hgpu

Benchmarking State-of-the-Art Deep Learning Software Tools

Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu

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Tags: Benchmarking, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, RNN, Tesla K80

August 28, 2016 by hgpu

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang

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Tags: CNN, CUDA, Deep learning, Image processing, Matlab, nVidia, nVidia GeForce GTX Titan X, Package, Signal denoising

August 18, 2016 by hgpu

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Balint Pal Toth, Balint Czeba

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan X, Speech recognition

August 16, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

Gopalakrishna Hegde, Siddhartha, Nachiappan Ramasamy, Nachiket Kapre

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Tags: Computer science, CUDA, Deep learning, DSP, FPGA, nVidia, nVidia Tegra TX1

August 11, 2016 by hgpu

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Zhehuan Zhao, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

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Tags: Bioinformatics, Biology, CUDA, Deep learning, Neural networks, NLP, nVidia, Package, Python, Tesla K20, Text mining

August 1, 2016 by hgpu

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda

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Tags: Algorithms, ARM, ASIC, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Tegra TX1

July 20, 2016 by hgpu

IODA: an Input/Output Deep Architecture for image labeling

J. Lerouge, R. Herault, Clement Chatelain, F. Jardin, R. Modzelewski

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python, Tesla C1060, Theano

July 18, 2016 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks

Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs

DeepPy: Pythonic deep learning

Deep Learning on FPGAs

Benchmarking State-of-the-Art Deep Learning Software Tools

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Learning Structured Sparsity in Deep Neural Networks

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

IODA: an Input/Output Deep Architecture for image labeling

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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