Adaptive computation of the corner singularity with the monotone jump condition capturing scheme

Author(s):  
Yin-Liang Huang ◽  
Wei-Cheng Wang
2018 ◽  
Vol 14 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Jérémy Barbay ◽  
Pablo Pérez-Lantero
Keyword(s):  

2020 ◽  
Author(s):  
Yuxiang Wu ◽  
Pasquale Minervini ◽  
Pontus Stenetorp ◽  
Sebastian Riedel

2005 ◽  
Vol 4 (2) ◽  
pp. 357-366
Author(s):  
Donatella Danielli ◽  
◽  
Marianne Korten ◽  

2007 ◽  
Vol 7 (3) ◽  
pp. 184-208
Author(s):  
W. Hall

The cluster state model for quantum computation [Phys. Rev. Lett. \textbf{86}, 5188] outlines a scheme that allows one to use measurement on a large set of entangled quantum systems in what is known as a cluster state to undertake quantum computations. The model itself and many works dedicated to it involve using entangled qubits. In this paper we consider the issue of using entangled qudits instead. We present a complete framework for cluster state quantum computation using qudits, which not only contains the features of the original qubit model but also contains the new idea of adaptive computation: via a change in the classical computation that helps to correct the errors that are inherent in the model, the implemented quantum computation can be changed. This feature arises through the extra degrees of freedom that appear when using qudits. Finally, for prime dimensions, we give a very explicit description of the model, making use of mutually unbiased bases.


Author(s):  
Kaoutar El Maghraoui ◽  
Joseph E. Flaherty ◽  
Boleslaw K. Szymanski ◽  
James D. Teresco ◽  
Carlos Varela

Author(s):  
Zhenyu Qi ◽  
Yan Zhang ◽  
Mircea Stan

Corner-based design and verification are based on worst-case analysis, thus introducing over-pessimism and large area and power overhead and leading to unnecessary energy consumption. Typical case-based design and verification maximize energy efficiency through design margins reduction and adaptive computation, thus helping achieve sustainable computing. Dynamically adapting to manufacturing, environmental, and usage variations is the key to shaving unnecessary design margins, which requires on-chip modules that can sense and configure design parameters both globally and locally to maximize computation efficiency, and maintain this efficiency over the lifetime of the system. This chapter presents an adaptive threshold compensation scheme using a transimpedance amplifier and adaptive body biasing to overcome the effects of temperature variation, reliability degradation, and process variation. The effectiveness and versatility of the scheme are demonstrated with two example applications, one as a temperature aware design to maintain IONto IOFFcurrent ratio, the other as a reliability sensor for NBTI (Negative Bias Temperature Instability).


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