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Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

Kade M. Heckel, Thomas Nowotny

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package, Python

March 3, 2024 by hgpu

TransAxx: Efficient Transformers with Approximate Computing

Dimitrios Danopoulos, Georgios Zervakis, Dimitrios Soudris, Jörg Henkel

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia V100, Package, PyTorch

February 18, 2024 by hgpu

Training DNN Models over Heterogeneous Clusters with Optimal Performance

Chengyi Nie, Jessica Maghakian, Zhenhua Liu

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, load balancing, Neural networks, nVidia, nVidia RTX 6000 Ada

February 12, 2024 by hgpu

A Heterogeneous Inference Framework for a Deep Neural Network

Rafael Gadea-Gironés, José Luís Rocabado-Rocha, Jorge Fe, Jose M. Monzo

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Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Heterogeneous systems, HLS, Machine learning, Neural networks, OpenCL, PyTorch

January 28, 2024 by hgpu

Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications

Foteini Strati, Xianzhe Ma, Ana Klimovic

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia A100, nVidia V100, Package, PyTorch

January 14, 2024 by hgpu

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Fabrizio Ferrandi, Serena Curzel, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Alessio Burrello, Francesco Barchi, Luca Benini, Luciano Lavagno, Teodoro Urso, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Nicola Petra, Davide De Caro, Gennaro Di Meo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Francesco Silvestri, Paolo Palazzari, Stefania Perri

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Tags: AI, Artificial intelligence, Computer science, CUDA, Deep learning, Design space exploration, Hardware Architecture, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia H100, OpenCL, survey

December 3, 2023 by hgpu

Frameworks in Medical Image Analysis with Deep Neural Networks

Dominik Müller

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Tags: Computed tomography, CT, Deep learning, Image processing, Medicine, Microscopy, Neural networks, nVidia, nVidia Titan RTX, Package, Python, Thesis, Tomography

November 27, 2023 by hgpu

Solving MaxSAT with Matrix Multiplication

David Warde-Farley, Vinod Nair, Yujia Li, Ivan Lobov, Felix Gimeno, Simon Osindero

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Tags: AI, Artificial intelligence, Computer science, Matrix multiplication, Neural networks, TPU

November 12, 2023 by hgpu

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

Arpan Jain

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Tags: Artificial intelligence, Computer science, CUDA, Data parallelism, Deep learning, HPC, Machine learning, MPI, Neural networks, nVidia, Speech recognition, Tesla K80, Tesla V100, Thesis

October 1, 2023 by hgpu

Compressed Real Numbers for AI: a case-study using a RISC-V CPU

Federico Rossi, Marco Cococcioni, Roger Ferrer Ibàñez, Jesùs Labarta, Filippo Mantovani, Marc Casas, Emanuele Ruffaldi, Sergio Saponara

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Tags: AI, Compression, Computer science, Machine learning, Neural networks

September 24, 2023 by hgpu

A Model Extraction Attack on Deep Neural Networks Running on GPUs

Jonah G. O'Brien Weiss

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Tags: Computer science, Neural networks, nVidia, nVidia Quadro RTX 8000, Security, Tesla T4

August 13, 2023 by hgpu

Isolated Scheduling for Distributed Training Tasks in GPU Clusters

Xinchi Han, Weihao Jiang, Peirui Cao, Qinwei Yang, Yunzhuo Liu, Shuyao Qi, Shengkai Lin, Shizhen Zhao

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Tags: Computer science, GPU cluster, Machine learning, Neural networks, nVidia, nVidia V100, Package

August 13, 2023 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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