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APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Boyuan Feng, Yuke Wang, Tong Geng, Ang Li, Yufei Ding

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 3090, Package, Precision, Python, Tesla A100

June 27, 2021 by hgpu

Tools for Reduced Precision Computation: A Survey

Stefano Cherubin, Giovanni Agosta

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Tags: Approximate computing and storage, Computer science, CUDA, HPC, Mixed precision, nVidia, OpenCL, Precision, survey

May 3, 2020 by hgpu

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Muhammad Mohid Nabil

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Tags: Computer science, CUDA, Deep learning, FPGA, HLS, Image recognition, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Precision, Thesis

May 12, 2019 by hgpu

Quasi-maximum Accuracy Floating-point Computations with GPGPU for Applications in Digital Signal Processing

Adam Dabrowski, Pawel Pawlowski, Mateusz Stankiewicz, Filip Misiorek

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Tags: CUDA, nVidia, nVidia Quadro FX 580, Precision, Signal processing

December 24, 2011 by hgpu

Precision and Performance: Floating Point and IEEE 754 Compliance for NVIDIA GPUs

Nathan Whitehead, Alex Fit-Florea

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Tags: Computer science, CUDA, nVidia, Precision

October 26, 2011 by hgpu

HLS Portability from Intel to Xilinx: A Case Study

HEXL: Intel Homomorphic Encryption Acceleration Library

Accelerating Encrypted Computing on Intel GPUs

Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

CompilerGym: a library performant reinforcement learning environments for compiler tasks

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

HPCToolkit

Measurement and Analysis of GPU-accelerated Applications with HPCToolkit

NVPTX-SPIR-V Translator

Supporting CUDA for an extended RISC-V GPU architecture

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high performance computing on graphics processing units: hgpu.org

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Tools for Reduced Precision Computation: A Survey

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Quasi-maximum Accuracy Floating-point Computations with GPGPU for Applications in Digital Signal Processing

Precision and Performance: Floating Point and IEEE 754 Compliance for NVIDIA GPUs

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