Study II. Spatially Explicit Hyperparameter Optimization of Neural Networks Accelerated Using High-Performance Computing

2021 ◽  
pp. 69-80
Author(s):  
Minrui Zheng
Keyword(s):  
Neural Networks ◽  
High Performance Computing ◽  
High Performance ◽  
Spatially Explicit ◽  
Hyperparameter Optimization ◽  
Performance Computing

scholarly journals Silicon photonics for artificial intelligence applications

Photoniques ◽  
2020 ◽  
pp. 40-44
Author(s):  
Bicky A. Marquez ◽  
Matthew J. Filipovich ◽  
Emma R. Howard ◽  
Viraj Bangari ◽  
Zhimu Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Efficiency ◽  
High Performance Computing ◽  
Silicon Photonics ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Neuromorphic Engineering ◽  
Software Implementations ◽  
Performance Computing

Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.

scholarly journals Aspects of programming for implementation of convolutional neural networks on multisystem HPC architectures

2021 ◽  
Vol 2062 (1) ◽  
pp. 012016
Author(s):  
Sunil Pandey ◽  
Naresh Kumar Nagwani ◽  
Shrish Verma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance Computing ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Processing Unit ◽  
Computational Performance ◽  
Performance Computing

Abstract The training of deep learning convolutional neural networks is extremely compute intensive and takes long times for completion, on all except small datasets. This is a major limitation inhibiting the widespread adoption of convolutional neural networks in real world applications despite their better image classification performance in comparison with other techniques. Multidirectional research and development efforts are therefore being pursued with the objective of boosting the computational performance of convolutional neural networks. Development of parallel and scalable deep learning convolutional neural network implementations for multisystem high performance computing architectures is important in this background. Prior analysis based on computational experiments indicates that a combination of pipeline and task parallelism results in significant convolutional neural network performance gains of up to 18 times. This paper discusses the aspects which are important from the perspective of implementation of parallel and scalable convolutional neural networks on central processing unit based multisystem high performance computing architectures including computational pipelines, convolutional neural networks, convolutional neural network pipelines, multisystem high performance computing architectures and parallel programming models.

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