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YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini

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Tags: Computer science, Computer vision, Deep learning, Hardware Architecture, Neural networks

June 21, 2016 by hgpu

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

Hugh Perkins

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Tags: Algorithms, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Package

June 21, 2016 by hgpu

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Re

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

June 16, 2016 by hgpu

Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

Matthew Moskewicz, Forrest Iandola, Kurt Keutzer

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Tags: ARM, CNN, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, Neural networks, OpenCL

June 7, 2016 by hgpu

Co-tuning of Software Specializers and Hardware Accelerators within a CNN Application

Sean Roberts

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Tags: ARM, CNN, Computer science, Deep learning, FPGA, Neural networks, SoC

June 7, 2016 by hgpu

Weighted Residuals for Very Deep Networks

Falong Shen, Gang Zeng

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X

June 2, 2016 by hgpu

TensorFlow: A system for large-scale machine learning

Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zhang

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeFofce GTX Titan X, Package, Tesla K40

May 30, 2016 by hgpu

Deep API Learning

Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim

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Tags: Computer science, CUDA, Deep learning, Information Retrieval, NLP, nVidia, RNN, Tesla K20

May 30, 2016 by hgpu

Theano-MPI: a Theano-based Distributed Training Framework

He Ma, Fei Mao, Graham W. Taylor

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Tags: Computer science, CUDA, Deep learning, Machine learning, MPI, nVidia, Package, Tesla K20, Tesla K80

May 28, 2016 by hgpu

Learning a Metric Embedding for Face Recognition using the Multibatch Method

Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua

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Tags: Algorithms, ARM, Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Performance

May 26, 2016 by hgpu

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

Matthew Dixon, Diego Klabjan, Jin Hoon Bang

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Tags: Algorithms, CNN, Computer science, Deep learning, Finance, Intel Xeon Phi, Neural networks

May 26, 2016 by hgpu

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Philipp Gysel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K40, Thesis

May 23, 2016 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

microSYCL: SYCL micro-benchmarks repository

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

XaaS containers

Acceleration as a Service (XaaS) Source Containers

SYCL Container

Exploring SYCL for batched kernels with memory allocations

CASS: Cuda-Amd aSSembly

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Cluser of smartphones for edge computing application using TensorFlow

Low-cost edge computing using upcycled smartphones

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Can Large Language Models Predict Parallel Code Performance?

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