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Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

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Tags: Computer science, Deep learning, FPGA, Neural networks, nVidia, Package, PTX

February 3, 2018 by hgpu

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Xinying Wang

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Tags: Computer science, Data mining, Factorization, FPGA, Intel Xeon Phi, Matrix decomposition, Semantic indexing, Signal processing, Text mining, Thesis

December 28, 2017 by hgpu

A Survey of FPGA Based Neural Network Accelerator

Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang

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Tags: Computer science, Computer vision, FPGA, Machine learning, Neural networks

December 28, 2017 by hgpu

Pass a Pointer: Exploring Shared Virtual Memory Abstractions in OpenCL Tools for FPGAs

Felix Winterstein, George Constantinides

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Package

December 24, 2017 by hgpu

Tactics to Directly Map CNN graphs on Embedded FPGAs

Kamel Abdelouahab, Maxime Pelcat, Cedric Bourrasset, Francois Berry, Jocelyn Serot

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Tags: Caffe, Computer science, Deep learning, FPGA, Neural networks, Package

December 19, 2017 by hgpu

FPGA-Accelerated Image Processing Using High Level Synthesis with OpenCL

Johan Isaksson

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Tags: FPGA, Image processing, OpenCL, Thesis

December 10, 2017 by hgpu

A Hybrid-parallel Architecture for Applications in Bioinformatics

Jan Christian Kassens

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 780 Ti, Thesis

December 3, 2017 by hgpu

fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs

Stylianos I. Venieris, Christos-Savvas Bouganis

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Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Neural networks

November 30, 2017 by hgpu

Intel FPGA SDK for OpenCL

Intel Corporation

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Tags: Computer science, FPGA, OpenCL, Package, Whitepaper

November 30, 2017 by hgpu

High Performance Streaming Smith-Waterman Implementation with Implicit Synchronization on Intel FPGA using OpenCL

Ernst Joachim Houtgast, Vlad-Mihai Sima, Zaid Al-Ars

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Tags: Algorithms, Bioinformatics, Biology, FPGA, OpenCL, Smith-Waterman algorithm

November 26, 2017 by hgpu

Compiling and Optimizing OpenMP 4.X Programs to OpenCL and SPIR

Marcio M. Pereira, Rafael C. F. Sousa, Guido Araujo

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Tags: ARM, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, LLVM, nVidia, OpenCL, OpenMP, Package, Tesla K40

November 21, 2017 by hgpu

Unified Deep Learning with CPU, GPU, and FPGA Technologies

Allen Rush, Ashish Sirasao, Mike Ignatowski

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Machine learning, OpenCL

November 21, 2017 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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