This project is focused on measuring the execution time, the energy consumption and the performance of the new instruction set introduced by Intel in the Cascade Lake series of processors, which are called Vector Neural Network Instructions (VNNI). These instructions are part of the AVX512 instruction set, and they are specifically designed to accelerate deep learning codes. To analyse the performance of these instructions, a set of benchmarks will have to be designed and developed. In addition, the impact of using these instructions inside HPC containers will be also evaluated because HPC clusters are the natural place to use these high-end architecture
The simulation of detailed neuronal circuits is based on computationally expensive software simulati...
This report presents a set of results for different microbenchmarks and applications on the Intel X...
The primary outcome of this research project is the development of a methodology enabling fast autom...
In this paper we discuss new Intel instruction extensions - Intel Advance Vector Extensions 2 (AVX2)...
A number of recent researches focus on designing accelerators for popular deep learning algorithms. ...
En este estudio analizo el proceso de entrenamiento de una red neural convolucional desde la perspec...
Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to...
AbstractIn this paper we take a look at what the new Intel instruction extensions - Intel Advance Ve...
Indiana University-Purdue University Indianapolis (IUPUI)Performance models are useful as mathematic...
Quintillions of bytes of data are generated every day in this era of big data. Machine learning tech...
Recently, renewed attention to Artificial Intelligence has emerged thanks to algorithms called Deep ...
International audienceThis paper contributes towards better understanding the energy consumption tra...
There is a well-known spectrum of computing hardware ranging from central processing units (CPUs) to...
International audienceDeep neural networks (DNNs) are computationally and memory intensive, which ma...
With respect to the continuous growth of computing systems, the energy-efficiency requirement of the...
The simulation of detailed neuronal circuits is based on computationally expensive software simulati...
This report presents a set of results for different microbenchmarks and applications on the Intel X...
The primary outcome of this research project is the development of a methodology enabling fast autom...
In this paper we discuss new Intel instruction extensions - Intel Advance Vector Extensions 2 (AVX2)...
A number of recent researches focus on designing accelerators for popular deep learning algorithms. ...
En este estudio analizo el proceso de entrenamiento de una red neural convolucional desde la perspec...
Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to...
AbstractIn this paper we take a look at what the new Intel instruction extensions - Intel Advance Ve...
Indiana University-Purdue University Indianapolis (IUPUI)Performance models are useful as mathematic...
Quintillions of bytes of data are generated every day in this era of big data. Machine learning tech...
Recently, renewed attention to Artificial Intelligence has emerged thanks to algorithms called Deep ...
International audienceThis paper contributes towards better understanding the energy consumption tra...
There is a well-known spectrum of computing hardware ranging from central processing units (CPUs) to...
International audienceDeep neural networks (DNNs) are computationally and memory intensive, which ma...
With respect to the continuous growth of computing systems, the energy-efficiency requirement of the...
The simulation of detailed neuronal circuits is based on computationally expensive software simulati...
This report presents a set of results for different microbenchmarks and applications on the Intel X...
The primary outcome of this research project is the development of a methodology enabling fast autom...