Low-Energy Instruction Precision Assignment for Multi-mode Multiplier Under Accuracy and Performance Constraints

2014 ◽  
Vol 40 (3) ◽  
pp. 787-798
Author(s):  
S.-R. Kuang ◽  
K.-Y. Wu
Author(s):  
Gaurav Chaurasia ◽  
Arthur Nieuwoudt ◽  
Alexandru-Eugen Ichim ◽  
Richard Szeliski ◽  
Alexander Sorkine-Hornung

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.


2009 ◽  
Vol 37 (1) ◽  
pp. 1-30 ◽  
Author(s):  
C. Alan Short ◽  
Malcolm Cook ◽  
Kevin J. Lomas

2018 ◽  
Vol 232 ◽  
pp. 292-311 ◽  
Author(s):  
Eduardo González-Gorbeña ◽  
André Pacheco ◽  
Theocharis A. Plomaritis ◽  
Óscar Ferreira ◽  
Cláudia Sequeira

2018 ◽  
Vol 53 (10) ◽  
pp. 628-633 ◽  
Author(s):  
Kathryn E Ackerman ◽  
Bryan Holtzman ◽  
Katherine M Cooper ◽  
Erin F Flynn ◽  
Georgie Bruinvels ◽  
...  

Low energy availability (EA) is suspected to be the underlying cause of both the Female Athlete Triad and the more recently defined syndrome, Relative Energy Deficiency in Sport (RED-S). The International Olympic Committee (IOC) defined RED-S as a syndrome of health and performance impairments resulting from an energy deficit. While the importance of adequate EA is generally accepted, few studies have attempted to understand whether low EA is associated with the health and performance consequences posited by the IOC.ObjectiveThe purpose of this cross-sectional study was to examine the association of low EA with RED-S health and performance consequences in a large clinical population of female athletes.MethodsOne thousand female athletes (15–30 years) completed an online questionnaire and were classified as having low or adequate EA. The associations between low EA and the health and performance factors listed in the RED-S models were evaluated using chi-squared test and the odds ratios were evaluated using binomial logistic regression (p<0.05).ResultsAthletes with low EA were more likely to be classified as having increased risk of menstrual dysfunction, poor bone health, metabolic issues, haematological detriments, psychological disorders, cardiovascular impairment and gastrointestinal dysfunction than those with adequate EA. Performance variables associated with low EA included decreased training response, impaired judgement, decreased coordination, decreased concentration, irritability, depression and decreased endurance performance.ConclusionThese findings demonstrate that low EA measured using self-report questionnaires is strongly associated with many health and performance consequences proposed by the RED-S models.


1995 ◽  
Author(s):  
Peter Schwenn ◽  
George Hazen

We describe some advances in Performance Prediction Programs - "PPP"1 for sailing yachts2 - primarily integrating PPP analysis into drawing and providing new sculpting operations in which fairness and desired hydrostatic and on her performance determining characteristics are maintained - the shape remains a boat or a ship of the desired kind during reshaping. Our building blocks for such an integration are: a thousand-fold increase in PPP speed3, new editing tools which maintain Boatness4 , and an accessible modularization of the engineering physics of the PPP within a new programming environment which allows immediate changes by designers. Specifically, these new functions are introduced at the boundary of Drawing and the PPP: - A live knotmeter is displayed with each design variant on the drawing boar, - alongside it's antagonist - Rating. - Continuously updated hydrotatics (including the speed determining factors LSM, wetted surface, stability, prismatics, .. ) are displayed with the knotometer, with the 'positive' factors (like length) graphically opposing the 'negative' (like wetted surface.) Dimensions for PPP use are calculated automatically from the shape at hand - in particular: appendage dimensions, hydrostatics, and so forth. - Bounding limits are set for a design optimization by drawing two or more outlier yacht forms. The space in between can be explored by hand or automatically. - Local optimums of Speed against rating are provided as a 'Snap' function. This is the one dimensional version of automatic exploration for optima. - Intermediate shapes are also controlled during design optimization to maintain realism and performance constraints on type, fairness, 'look', speed producing shape measures like prismatic and displacement etc., and even handicap. - Immediate feedback is available if one chooses to exploit the new programming environment to make aero hydro model changes or extensions to the internal PPP mechanisms while drawing and exploring.


2019 ◽  
Vol 116 ◽  
pp. 9-19 ◽  
Author(s):  
Lixiao Chen ◽  
Jie Gao ◽  
Qi Wu ◽  
Hui Li ◽  
Shuqing Dong ◽  
...  

Author(s):  
Elena Fantino ◽  
Francisco Salazar ◽  
Elisa Maria Alessi
Keyword(s):  

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