Zobrazeno 1 - 10
of 296
pro vyhledávání: '"Performance per watt"'
Publikováno v:
IEEE Access, Vol 4, Pp 108-118 (2016)
As the smart cities emerged for more comfortable urban spaces, services, such as health, transportation, and so on, need to be promoted. In addition, the cloud computing provides flexible allocation, migration of services, and better security isolati
Externí odkaz:
https://doaj.org/article/bf27a34c97234553a2d1b27302140eea
Publikováno v:
Cluster Computing
Green computing is an important factor to ensure the eco-friendly use of computers and their resources. Electric power used in a computer converts into heat and thus, the system takes fewer watts ensuring less cooling. This lower energy consumption a
Autor:
Rajit Manohar, Michael Wu, Abhishek Bhattacharjee, Jan Vesely, Karthik Sriram, Xiayuan Wen, Nick Lindsay, David A. Borton, Ioannis Karageorgos, Marc Powell
Publikováno v:
IEEE Micro. 41:87-94
We are building HALO, a flexible ultralow-power processing architecture for implantable brain– computer interfaces (BCIs) that directly communicate with biological neurons in real time. This article discusses the rigid power, performance, and flexi
Autor:
Vivek Bhardwaj
Publikováno v:
International Journal of Computer Applications. 174:45-49
Field Programmable Gate Arrays have long been seen as a viable alternative to Application Specific Integrated Circuits (ASICs) . While ASICs have very sophisticated commercialized EDA tools that deliver very fast and power efficient chips, the FPGA w
Autor:
Tarek Belabed, Maria Gracielly F. Coutinho, Marcelo A. C. Fernandes, Carlos Valderrama Sakuyama, Chokri Souani
Publikováno v:
IEEE Access, Vol 9, Pp 89162-89180 (2021)
Deep Learning techniques have been successfully applied to solve many Artificial Intelligence (AI) applications problems. However, owing to topologies with many hidden layers, Deep Neural Networks (DNNs) have high computational complexity, which make
Publikováno v:
ACM SIGBED Review. 17:56-62
In the last few years Internet of Things (IoT) applications are moving from the cloud-sensor paradigm to a more variegated structure where IoT nodes interact with an intermediate fog computing layer. To enable compute-intensive tasks to be executed n
Autor:
Tor M. Aamodt, Scott Peverelle, Amogh Manjunath, Vijay Kandiah, Junrui Pan, Mahmoud Khairy, Timothy G. Rogers, Nikos Hardavellas
Publikováno v:
MICRO
Graphics Processing Units (GPUs) are rapidly dominating the accelerator space, as illustrated by their wide-spread adoption in the data analytics and machine learning markets. At the same time, performance per watt has emerged as a crucial evaluation
Publikováno v:
DSD
The computational intensity in embedded processing applications is increasing. This requires domain-specific embedded platforms in order to achieve maximum performance per watt of the system. With the arrival of open-source instruction set architectu
Publikováno v:
Lecture Notes in Computer Science
EURO-PAR 2021-27th International European Conference on Parallel and Distributed Computing
EURO-PAR 2021-27th International European Conference on Parallel and Distributed Computing, Aug 2021, Lisbon, Portugal. pp.334-349, ⟨10.1007/978-3-030-85665-6_21⟩
Euro-Par 2021: Parallel Processing ISBN: 9783030856649
Euro-Par
EURO-PAR 2021-27th International European Conference on Parallel and Distributed Computing
EURO-PAR 2021-27th International European Conference on Parallel and Distributed Computing, Aug 2021, Lisbon, Portugal. pp.334-349, ⟨10.1007/978-3-030-85665-6_21⟩
Euro-Par 2021: Parallel Processing ISBN: 9783030856649
Euro-Par
Production high-performance computing systems continue to grow in complexity and size. As applications struggle to make use of increasingly heterogeneous compute nodes, maintaining high efficiency (performance per watt) for the whole platform becomes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1552f781f8e4cd515f259fb9c8c22c4
https://hal.inria.fr/hal-03259316
https://hal.inria.fr/hal-03259316
Publikováno v:
FPL
Spiking neural networks (SNN) with their ‘integrate and fire’ (I&F) neurons replace the hardware-intensive multiply-accumulate (MAC) operations in convolutional neural networks (CNN) with accumulate operations — not only making it easy to imple