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Performance Debugging Frameworks for FPGA High-Level Synthesis

Young-kyu Choi

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Tags: Benchmarking, Computer science, Design space exploration, FPGA, HLS, Performance, Thesis

October 27, 2019 by hgpu

Fast Code Exploration for Pipeline Processing in FPGA Accelerators

Leandro de Souza Rosa

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Tags: Computer science, Design space exploration, FPGA, HLS, Thesis

September 8, 2019 by hgpu

Compilers for Portable Programming of Heterogeneous Parallel & Approximate Computing Systems

Prakalp Srivastava

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Tags: Code generation, Computer science, Design space exploration, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability

September 8, 2019 by hgpu

High Performance Computing via High Level Synthesis

Mehdi Roozmeh

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, Nearest neighbour, nVidia, OpenCL, Package, Signal processing, Thesis

August 18, 2019 by hgpu

Software Platform for Hybrid Resource Management of Many-core Accelerators

Taeyoung Kim

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Tags: Computer science, Design space exploration, Intel Xeon Phi, OpenCL, Thesis

December 16, 2018 by hgpu

Efficient Hardware Acceleration on SoC-FPGA with OpenCL

Susmitha Gogineni

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, OpenCL, Thesis

May 12, 2018 by hgpu

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis

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Tags: Artificial intelligence, Computer science, Deep learning, Design space exploration, FPGA, OpenCL, survey

March 17, 2018 by hgpu

Analysing the Performance of GPU Hash Tables for State Space Exploration

Nathan Cassee, Anton Wijs

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Tags: Computer science, CUDA, Design space exploration, Hashing, nVidia, nVidia GeForce GT 750 M, Performance

January 6, 2018 by hgpu

Efficient Target and Application Specific Selection and Ordering of Compiler Passes

Ricardo Jorge Ferreira Nobre

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Tags: Code generation, Compilers, Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX 460, nVidia GeForce GTX 480, OpenCL, PTX, Thesis

November 26, 2017 by hgpu

Exploration of cyber-physical systems for GPGPU computer vision-based detection of biological viruses

Pascal Libuschewski

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Tags: Biology, Computer vision, Design space exploration, Microscopy, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 560 Ti, nVidia Quadro NVS 3100 M, OpenCL, Thesis, Visualization

April 20, 2017 by hgpu

Efficient Kernel Synthesis for Performance Portable Programming

Li-Wen Chang, Izzat El Hajj, Christopher Rodrigues, Juan Gomez-Luna, Wen-mei Hwu

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Tags: Code generation, Computer science, CUDA, Design space exploration, Heterogeneous systems, nVidia, performance portability, Tesla C2050, Tesla K20

November 25, 2016 by hgpu

Many-Core Architectures: Hardware-Software Optimization and Modeling Techniques

Christian Pinto

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Tags: Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce GTX 480, Thesis, Virtualization

July 17, 2015 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Masivo: Parallel public transport routes simulation

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

FBLAS: BLAS implementation for Intel FPGA

FBLAS: Streaming Linear Algebra Kernels on FPGA

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Titan: A CUDA-based physics simulation sandbox for high-performance robotics, physics, and reinforcement learning applications

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

onYourMarks: A deep learning benchmark implementation for Nvidia's Jetson TX2

Benchmarking Deep Learning Models on Jetson TX2

Hacking Neural Networks: A small course on exploiting and defending neural networks

Hacking Neural Networks: A Short Introduction

A Computing Kernel for Network Binarization on PyTorch

A Computing Kernel for Network Binarization on PyTorch

Deep Learning Based FPGA-CPU Acceleration

Deep Learning Based FPGA-CPU Acceleration

KTT: Kernel Tuning Toolkit

Framework for Parallel Kernels Auto-tuning

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

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