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 Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1029
Author(s):  
Anabi Hilary Kelechi ◽  
Mohammed H. Alsharif ◽  
Okpe Jonah Bameyi ◽  
Paul Joan Ezra ◽  
Iorshase Kator Joseph ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficiency ◽  
High Performance Computing ◽  
High Performance ◽  
Large Data ◽  
Computing Systems ◽  
Computing Power ◽  
Product Delivery ◽  
High Level ◽  
Performance Computing

Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.

Energy Efficiency and High-Performance Computing

2015 ◽  
pp. 197-224 ◽  
Author(s):  
Pascal Bouvry ◽  
Ghislain Landry Tsafack Chetsa ◽  
Georges Da Costa ◽  
Emmanuel Jeannot ◽  
Laurent Lefèvre ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
High Performance Computing ◽  
High Performance ◽  
Performance Computing

A holistic model of the performance and the energy efficiency of hypervisors in a high-performance computing environment

2014 ◽  
Vol 26 (15) ◽  
pp. 2569-2590 ◽  
Author(s):  
Mateusz Guzek ◽  
Sébastien Varrette ◽  
Valentin Plugaru ◽  
Johnatan E. Pecero ◽  
Pascal Bouvry
Keyword(s):  
Energy Efficiency ◽  
High Performance Computing ◽  
High Performance ◽  
Computing Environment ◽  
Holistic Model ◽  
Performance Computing
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