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Reinforcement Learning Strategies for Compiler Optimization in High level Synthesis

Hafsah Shahzad, Ahmed Sanaullah, Sanjay Arora, Robert Munafo, Xiteng Yao, Ulrich Drepper, Martin Herbordt

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Tags: Compilers, Computer science, FPGA, HLS, LLVM, Machine learning

March 26, 2023 by hgpu

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

Mahdi Taheri, Mohamad Riazati, Mohammad Hasan Ahmadilivani, Maksim Jenihhin, Masoud Daneshtalab, Jaan Raik, Mikael Sjodin, Bjorn Lisper

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Tags: Computer science, Deep learning, Design space exploration, FPGA, HLS, Neural networks, Package

March 5, 2023 by hgpu

HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis

Zhigang Wei, Aman Arora, Lizy K. John

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Tags: Computer science, FPGA, Hardware Architecture, HLS, Machine learning, Package

February 26, 2023 by hgpu

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Peter Knoben, Nikolaos Alachiotis

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Tags: Bioinformatics, Biology, Computer science, FPGA, OpenCL, Package

February 12, 2023 by hgpu

Optimization of massive data applications on heterogeneous architectures

José Carlos Romero Moreno

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Tags: Computer science, FPGA, Heterogeneous systems, Intel, Intel UHD 630, OpenCL, Package, SYCL, Thesis

February 5, 2023 by hgpu

Pulsar search acceleration using FPGAs and OpenCL templates

Julian Oppermann, Mitchell B. Mickaliger, Oliver Sinnen

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Tags: Astrophysics, Design space exploration, FPGA, OpenCL, Package, Physics, Signal processing

January 29, 2023 by hgpu

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Manuel de Castro, Roberto R. Osorio, David L. Vilariño, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Computer science, FPGA, OpenCL, Package

January 29, 2023 by hgpu

Efficient OpenCL system integration of non-blocking FPGA accelerators

Topi Leppänen, Atro Lotvonen, Panagiotis Mousouliotis, Joonas Multanen, Georgios Keramidas, Pekka Jääskeläinen

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

January 22, 2023 by hgpu

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi

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Tags: Bayesian, Compilers, Computer science, FPGA, nVidia, OpenCL, Package, Performance, Programming Languages, Tesla K80

January 8, 2023 by hgpu

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

Manos Pavlidakis, Stelios Mavridis, Antony Chazapis, Giorgos Vasiliadis, Angelos Bilas

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 2080, OpenCL, Package

January 8, 2023 by hgpu

mu-grind: A Framework for Dynamically Instrumenting HLS-Generated RTL

Parmida Vahdatniya, Amirali Sharifian, Reza Hojabr, Arrvindh Shriraman

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, Package, Scala

December 25, 2022 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

high performance computing on graphics processing units: hgpu.org

Reinforcement Learning Strategies for Compiler Optimization in High level Synthesis

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Optimization of massive data applications on heterogeneous architectures

Pulsar search acceleration using FPGAs and OpenCL templates

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Efficient OpenCL system integration of non-blocking FPGA accelerators

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

mu-grind: A Framework for Dynamically Instrumenting HLS-Generated RTL

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