VCAMS: Viterbi-Based Context Aware Mobile Sensing to Trade-Off Energy and Delay

2018 ◽  
Vol 17 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Sirine Taleb ◽  
Hazem Hajj ◽  
Zaher Dawy
Keyword(s):  
Mobile Sensing ◽  
Context Aware ◽  
Trade Off

scholarly journals A Context-Aware Library for Mathematical Approximations

2018 ◽  
Author(s):  
Roberto Alejandro Hidalgo Castro ◽  
Lucas Wanner
Keyword(s):  
Energy Consumption ◽  
Case Studies ◽  
Context Aware ◽  
Trade Off ◽  
System Service ◽  
Series Of Functions

We have built a mathematical library that includes a series of functions with different implementations with varying precision. We couple this library with a system service that monitors the computer context, including energy consumption, and according to this context, using specified rules, dynamically changes the implementations used by the target applications. Our case studies show that our library can trade-off at most of 4% degradation in application quality up to 40% savings in energy consumption.

scholarly journals Energy-efficient Amortized Inference with Cascaded Deep Classifiers

2018 ◽  
Author(s):  
Jiaqi Guan ◽  
Yang Liu ◽  
Qiang Liu ◽  
Jian Peng
Keyword(s):  
Neural Networks ◽  
Energy Cost ◽  
Deep Neural Networks ◽  
Predictive Accuracy ◽  
Computational Cost ◽  
Mobile Sensing ◽  
Test Time ◽  
Trade Off ◽  
Effective Cost ◽  
Energy Constrained

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing. We address this problem by proposing a novel framework that optimizes the prediction accuracy and energy cost simultaneously, thus enabling effective cost-accuracy trade-off at test time. In our framework, each data instance is pushed into a cascade of deep neural networks with increasing sizes, and a selection module is used to sequentially determine when a sufficiently accurate classifier can be used for this data instance. The cascade of neural networks and the selection module are jointly trained in an end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between the computational cost and the predictive accuracy. Our method is able to simultaneously improve the accuracy and efficiency by learning to assign easy instances to fast yet sufficiently accurate classifiers to save computation and energy cost, while assigning harder instances to deeper and more powerful classifiers to ensure satisfiable accuracy. Moreover, we demonstrate our method's effectiveness with extensive experiments on CIFAR-10/100, ImageNet32x32 and original ImageNet dataset.

scholarly journals Safer at any speed: automatic context-aware safety enhancement for Rust

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-23
Author(s):  
Natalie Popescu ◽  
Ziyang Xu ◽  
Sotiris Apostolakis ◽  
David I. August ◽  
Amit Levy
Keyword(s):  
Safety Performance ◽  
Context Aware ◽  
Trade Off ◽  
Extra Effort ◽  
Buffer Overflows ◽  
Development Tool ◽  
Relative Value ◽  
End To End ◽  
Application Developers ◽  
Safety Enhancement

Type-safe languages improve application safety by eliminating whole classes of vulnerabilities–such as buffer overflows–by construction. However, this safety sometimes comes with a performance cost. As a result, many modern type-safe languages provide escape hatches that allow developers to manually bypass them. The relative value of performance to safety and the degree of performance obtained depends upon the application context, including user goals and the hardware upon which the application is to be executed. Since libraries may be used in many different contexts, library developers cannot make safety-performance trade-off decisions appropriate for all cases. Application developers can tune libraries themselves to increase safety or performance, but this requires extra effort and makes libraries less reusable. To address this problem, we present NADER, a Rust development tool that makes applications safer by automatically transforming unsafe code into equivalent safe code according to developer preferences and application context. In end-to-end system evaluations in a given context, NADER automatically reintroduces numerous library bounds checks, in many cases making application code that uses popular Rust libraries safer with no corresponding loss in performance.

A Flow-Preserving Algorithm for the Time-Cost Trade-Off Problem

1982 ◽  
Vol 14 (2) ◽  
pp. 109-113 ◽  
Author(s):  
Suleyman Tufekci
Keyword(s):  
Time Cost ◽  
Trade Off

Dual Goals for Speed and Accuracy on the Same Performance Task

2012 ◽  
Vol 11 (3) ◽  
pp. 118-126 ◽  
Author(s):  
Olive Emil Wetter ◽  
Jürgen Wegge ◽  
Klaus Jonas ◽  
Klaus-Helmut Schmidt
Keyword(s):  
Memory Scanning ◽  
Performance Task ◽  
Performance Tasks ◽  
Trade Off ◽  
Test Experiment ◽  
Trade Offs ◽  
New Finding ◽  
Sternberg Paradigm ◽  
Speed Accuracy ◽  
Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

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